Patent Publication Number: US-10329882-B2

Title: Optimizing completion operations

Description:
BACKGROUND 
     When performing an oilfield operation, decisions are often complex because of the large number and kinds of considerations to be taken into account, including the uncertainty of the risks and rewards that may only be discovered during the operation. Risk and reward analysis is an important part of the decision-making process for oil exploration and production for several reasons. First, risk and reward analysis provides a means for prioritizing the large number and kinds of decisions. Next, risk and reward analysis provides an approach for balancing value tradeoffs and different preferences of the stakeholders in the decision process. For example, a balance may be achieved between the conflicting goals of drilling as fast as possible, maintaining integrity of the formation, and ensuring on-site safety. 
     Current modeling of risks and rewards lacks accuracy and flexibility in the face of changing conditions. Specifically, unexpected high-risk events are addressed in an ad-hoc manner during the operation, and unexpected rewards associated with little risk are not pursued. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Accordingly, there are disclosed herein certain oilfield operation optimization systems and methods. In the following detailed description of the various disclosed embodiments, reference will be made to the accompanying drawings in which: 
         FIG. 1  is a contextual view of an illustrative drilling environment; 
         FIG. 2  is a contextual view of an illustrative logging and cementing environment; 
         FIG. 3  is a contextual view of an illustrative hydraulic fracturing environment; 
         FIGS. 4  is a contextual view of an illustrative well completion environment; 
         FIG. 5  is a contextual view of an illustrative formation treatment environment; 
         FIG. 6A  is an illustration of a user interfacing with an illustrative risk and reward optimization system; 
         FIGS. 6B and 6C  are a block diagrams of illustrative risk and reward optimization systems; 
         FIG. 7  is block diagram of an illustrative control module configuration in a risk and reward optimization system; 
         FIG. 8  is flow diagram of an illustrative risk and reward optimization method; and 
         FIGS. 9A-9C  are diagrams of illustrative risk and reward scenarios. 
     
    
    
     It should be understood, however, that the specific embodiments given in the drawings and detailed description thereto do not limit the disclosure. On the contrary, they provide the foundation for one of ordinary skill to discern the alternative forms, equivalents, and modifications that are encompassed together with one or more of the given embodiments in the scope of the appended claims. 
     NOTATION AND NOMENCLATURE 
     Certain terms are used throughout the following description and claims to refer to particular system components and configurations. As one skilled in the art will appreciate, companies may refer to a component by different names. This document does not intend to distinguish between components that differ in name but not function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . ”. Also, the term “couple” or “couples” is intended to mean either an indirect or a direct electrical connection. Thus, if a first device couples to a second device, that connection may be through a direct electrical connection, or through an indirect electrical connection via other devices and connections. In addition, the term “attached” is intended to mean either an indirect or a direct physical connection. Thus, if a first device attaches to a second device, that connection may be through a direct physical connection, or through an indirect physical connection via other devices and connections. 
     DETAILED DESCRIPTION 
     The issues identified in the background are at least partly addressed by systems and methods for optimizing oilfield operations.  FIG. 1  shows an illustrative drilling environment. A drilling platform  2  supports a derrick  4  having a traveling block  6  for raising and lowering a bottomhole assembly (BHA)  19 . The platform  2  may also be located offshore for subsea drilling purposes in at least one embodiment. The BHA  19  may include one or more of a rotary steerable system, logging while drilling system, drill bit  14 , reamer, and downhole motor  26 . A top drive  10  supports and rotates the BHA  19  as it is lowered through the wellhead  12 . The drill bit  14  and reamer may also be driven by the downhole motor  26 . As the drill bit  14  and reamer rotate, they create a borehole  17  that passes through various formations  18 . A pump  20  circulates drilling fluid  24  through a feed pipe  22 , through the interior of the drill string to the drill bit  14 . The fluid exits through orifices in the drill bit  14  and flows upward to transport drill cuttings to the surface where the fluid is filtered and recirculated. 
     A data processing system  50  may be coupled to a measurement unit on the platform  2 , and may periodically obtain data from the measurement unit as a function of position and/or time. Software (represented by information storage media  52 ) may run on the data processing system  50  to collect the data and organize it in a file or database. The software may respond to user input via a keyboard  54  or other input mechanism to display data as an image or movie on a monitor  56  or other output mechanism. The software may process the data to optimize oilfield operations as described below. 
     In running operations such as drilling operations, short-term rewards may include weight-on-bit (WOB) and drillstring rotations-per-minute (RPM). A higher weight on the bit  14  and faster RPM are preferable as both are factors in increasing the rate of penetration (ROP) into the formation  18 . The short-term risks may include a region of vibration. Specifically, as the WOB and RPM increase, vibrations in the drillstring become increasingly likely. These vibrations interfere with the structural integrity of the drillstring components and also add noise to the drilling system. The long-term rewards may include an increase in ROP. The long-term rewards may also include a decrease in maximum dogleg severity. A dogleg is a bend in the borehole  17 . Dogleg severity is a measure of the amount of change in the inclination, and/or azimuth of the borehole  17 . By decreasing the dogleg severity, the strain on the drillstring and other downhole components is decreased. 
     In fluid management operations such as drilling fluid operations, long-term risks may include formation damage if the drilling fluid  24  does not prevent undesired formation fluid from entering the borehole  17 . Such formation damage may occur if the composition and density of the drilling fluid  24  is not tailored to the formation. Long-term rewards may include lowering the cost of drilling fluid, e.g., by changing the composition of the drilling fluid  24  to use cheaper ingredients, but still tailoring the drilling fluid  24  to the formation  18 . Long-term rewards may also include increasing the production rate of drilling fluid, i.e., increasing the rate at which useable drilling fluid is available. 
     In running operations such as hydraulic workover operations, where an underperforming well is reworked with hydraulic workover pipe to increase performance, short-term rewards may include increased hydraulic workover pipe insertion speed. The short-term risks may include release of downhole pressure during the workover operation. The long-term rewards may include increased hydraulic workover pipe insertion speed, which reduces the total time needed for the hydraulic workover operation. 
       FIG. 2  shows an illustrative logging and cementing environment. As sections of the borehole  212  are completed, the drill string may be removed from the borehole  212  and replaced by a casing string  200 . A cement slurry is pumped into the annular space between the casing string  200  and the wall of the borehole  212 , and the slurry hardens to form a cement sheath  201 . Ideally, the cement slurry displaces the drilling fluid and other materials from the annulus to form a continuous sheath that binds to the formation and tubing to seal the annulus against fluid flow. Various cement slurry compositions have been developed to provide various desirable features such as a density that can be tailored to avoid damage to the formation, a viscosity that is low enough to facilitate pumping and high enough to minimize mixing with other fluids, an ability to bind to the formation and casing material, and in some instances, a “self-healing” ability to seal any cracks that develop. Certain cement resin formulations offer an extremely adjustable set of properties. 
     Once the cementing job has been completed (i.e., the slurry has been pumped into position and allowed to set), a wireline logging suite is typically employed to evaluate the sheath and verify that the desired placement and sheath quality have been achieved. For example, a cement crew may verify that the previous materials have been displaced in the regions where formation fluid inflows might otherwise occur and that there are no bubbles, gaps, or flow paths along the sheath. 
     Next, a logging truck  202  may suspend a wireline logging sonde  204  on a wireline cable  206  having conductors for transporting power to the sonde and telemetry from the sonde to the surface. On the surface, a computer  208  acquires and stores measurement data from the logging tools in the sonde  204  as a function of position along the borehole and as a function of azimuth. The illustrated sonde  204  includes an ultrasonic scanning tool  216  and a cement bond logging (CBL) tool having an omnidirectional source  218 , an acoustic isolator  220 , an azimuthally-sensitive receiver  222 , and an omnidirectional receiver  224 . Centralizers  210  keep the sonde centered. The wireline sonde may further include an orientation module and a control/telemetry module for coordinating the operations of the various tools and communications between the various instruments and the surface. The ultrasonic scanning tool  216  has a rotating transceiver head that transmits ultrasonic pulses and receives reflected pulses to and from many points on the inner circumference of the casing. The amplitudes of the initial reflection from the inner surface of the casing and subsequent reflections from the outer surface of the casing and acoustic interfaces beyond the casing are indicative of the acoustic impedances of the casing and the annular materials beyond the casing. The acoustic interfaces can be mapped by tracking the travel time of each reflection. The CBL tool uses the acoustic source  218  to generate acoustic pulses that propagate along the casing string. The acoustic isolator  220  suppresses propagation of acoustic signals through the sonde itself. The receivers  222  and  224  detect the waveforms of the propagating acoustic signals, which have characteristics indicative of the quality of the cement sheath. For example, the maximum amplitude of the waveforms relative to the transmitted pulse varies with the quality of the bond between the casing and the cement. 
     In completion operations such as cementing operations, a short-term reward may include increased cement pumping rate, which decreases the total time needed for a cement job. The short-term risks may include formation of a bubble, uneven cement surface, and poor cement bond, which decrease the integrity of the cement. The long-term risks may include complete loss of cement integrity and low fracture gradient, which is the pressure required to fracture the cement. The long-term rewards may include increased cement integrity, decreased wait-on-cement time, and decreased material cost. For example, the type or formulation of cement may be tailored to the cement operation to decrease material cost. 
     In running operations such as logging operations, short-term risks may include increased measurement noise, which is undesirable because increased noise decreases the accuracy of the logging data. Short-term risks may also include biased measurements, which may result in persistent errors in the logging data. The long-term risks may include creation of inaccurate formation models and inaccurate reservoir models after completion of the logging operations. The long-term rewards may include increased logging speed, which decreases the total time needed for the logging operations, and increased logging resolution, which increases the accuracy of the logging data and models. 
       FIG. 3  shows an illustrative hydraulic fracturing environment in which a borehole  302  has been drilled into the target formation  300 . The borehole  302  has been cased with a casing  304  and cemented to sustain the structural integrity and stability of the borehole  302 . The target formation  300  may include multiple layers, each layer with a different type of rock formation, including the hydrocarbon-containing target formation within which the borehole may extend horizontally for some distance. The casing  304  contains multiple perforations  306  through which a fracturing fluid, such as water, is injected at high pressure into the target formation. This high-pressure fluid injection creates and opens fractures  308  that extend through the target formation. The high-pressure fluid may contain additional chemicals and materials, such as a proppant material (e.g., sand) that maintains the structural stability of the fractures and prevents the fractures from fully collapsing. Typically, the horizontal portions of the borehole are drilled generally parallel to the direction of maximum stress, causing the fractures to propagate generally perpendicular to the borehole. (As fractures tend to propagate perpendicular to the direction of maximum stress, such propagation may be expected to occur at a predictable angle from the borehole axis when the borehole is not aligned with the maximum stress direction.) The overlying and underlying formation layers tend to resist fracture propagation, consequently fractures tend to propagate laterally within the target formation, to a length that depends on the rate and volume of the injected fracturing fluid. Thus, each fracture has a length  310  relative to the casing  304 . Each fracture also has an initiation location  314  determined by the perforation position, which is typically measured relative to the distal end of the borehole  302 . Where regular spacing is employed, the perforations (and hence the fracture initiation points) have a fixed spacing  312  between them. 
     In stimulation operations such as hydraulic fracturing operations, the long-term risks may include unsuitable locations for hydraulic fracturing, e.g., if the formation  300  includes material that resists fracturing. The long-term risks may also include incompatibility between fracturing fluid and formation, wherein the proppants are ineffective or fracturing fluid damages or contaminates the formation, and proppant screen out, wherein the proppant prevents the desired hydrocarbons from entering the borehole. 
       FIG. 4  shows an illustrative well completion environment. Specifically,  FIG. 4  shows an example of a producer well with a borehole  402  that has been drilled into the earth. The producer well also includes a casing header  404  and a casing  406 , both secured into place by cement  403 . A blowout preventer (BOP)  408  couples to the casing header  404  and to a production wellhead  410 , which together seal in the well head and enable fluids to be extracted from the well in a safe and controlled manner 
     Measured well data is periodically sampled and collected from the producer well and combined with measurements from other wells within a reservoir, enabling the overall state of the reservoir to be monitored and assessed. These measurements may be taken using a number of different downhole and surface instruments, including but not limited to, a temperature and pressure sensor  418  and a flow meter  420 . Additional devices also coupled in line to a production tubing  412  include a downhole choke  416  (used to vary the fluid flow restriction), an electric submersible pump (ESP)  422  (which draws in fluid flowing from perforations  425  outside the ESP  422  and production tubing  412 ), an ESP motor  424  (to drive the ESP  322 ), and a packer  414  (isolating the production zone below the packer from the rest of the well). Additional surface measurement devices may be used to measure, for example, the tubing head pressure and the electrical power consumption of the ESP motor  424 . 
     Each of the devices along the production tubing  412  couples to a cable  428 , which is attached to the exterior of the production tubing  412  and is run to the surface through the blowout preventer  408  where it couples to a control panel  432 . The cable  428  provides power to the devices to which it couples, and further provides signal paths (electrical, optical, etc.) that enable control signals to be directed to the downhole devices, and for measurement signals to be received at the surface from the downhole devices. The devices may be controlled and monitored locally by field personnel using a user interface built into the control panel  432  coupled to an oilfield optimization system. Communication between control panel  432  and the oilfield optimization system may be via a wireless network (e.g., a cellular network), via a cabled network (e.g., a cabled connection to the Internet), or a combination of wireless and cabled networks. 
     If the formation  450  contains loose particulates  452  such as sands or soft sandstone, the particulates may migrate into the borehole through the perforations  425 , clogging the production system and eroding the devices along the production tubing  412 . To prevent this, fluid may be injected into the formation, and the fluid may react chemically or with heat to produce a permeable gel or solid to block the particulates  452  while allowing fluid flow. Also, a porous screen may be placed in the borehole between the production tubing  412  and the formation  450  wall. This technique is commonly referred to as gravel packing and the screen may include certain size rocks or gravel, Ottawa sand, walnut shells, glass beads, and the like. 
     In completion operations such as well completion operations, short-term rewards may include gravel-packing sand transport speed. A faster transport speed means that sand can be deployed to the annulus faster, thus decreasing total job time. Short-term risks may include thin gravel-packing carrier fluid and a sand dune effect, which occurs when an accumulation of sand decreases the production flow rate. The long-term risks may include damage to the reservoir. For example, the sands may enter the formation or reservoir and clog the conduit, thereby preventing hydrocarbons from escaping. The long-term rewards may include increased sand screening efficiency, wherein the porous screen or gel becomes more efficient, and an increase in borehole integrity. 
       FIG. 5  shows an illustrative formation treatment environment. While  FIG. 5  depicts a land-based system, like systems may be operated in subsea locations as well. A treatment fluid may be formulated in a mixing tank  502 . The treatment fluid may be conveyed via line  504  to a wellhead  506 , where the treatment fluid enters a tubular  508  extending from wellhead  506  into subterranean formation  510 . Upon being ejected from tubular  508 , the treatment fluid may subsequently penetrate into subterranean formation  510 . Pump  512  may be configured to raise the pressure of the treatment fluid to a desired degree before its introduction into tubular  508 . Various additional components may be present that have not necessarily been depicted in  FIG. 5  in the interest of clarity. Non-limiting additional components that may be present include, but are not limited to, supply hoppers, valves, condensers, adapters, joints, gauges, sensors, compressors, pressure controllers, pressure sensors, flow rate controllers, flow rate sensors, temperature sensors, and the like. Although not depicted in  FIG. 5 , the treatment fluid may, in some embodiments, flow back to wellhead  506  and exit subterranean formation  510 . In some embodiments, the treatment fluid that has flowed back to wellhead  506  may subsequently be recovered and recirculated to subterranean formation  510 . 
     In fluid management operations such as production chemical operations, the short-term rewards may include an increase in production rate of the treatment fluid and increase in damage removal rate. For example, scale may appear at the wellbore, restricting the hydrocarbon flow. Pumping an acid into the wellbore can dissolve such scale. The short-term risks may include a temperature change downhole, requiring reformulation of the treatment fluid, and a change in the composition of the reservoir fluid. The long-term risks may include clogging of the borehole, and the long-term rewards may include increased separation capability of reservoir fluids, e.g. separation of water and oil, increase in production rate, and decrease in treatment fluid, or chemical, cost. 
     As shown in  FIG. 6A , an analyst may employ a user interface  679  of a workstation  604  to view and/or control the optimization process. The workstation  604  is part of the hardware platform of an oilfield operation optimization system such as that shown in  FIG. 6B . The illustrative hardware platform couples the workstation  604  to one or more multi-processor computers  606  via a local area network (LAN)  605 . The one or more multi-processor computers  606  are in turn coupled via a storage area network (SAN)  608  to one or more shared storage units  610 . Using the personal workstation  604 , the analyst is able to load sensor and control data into the system, and to configure and monitor the processing of the sensor and control data. 
     Personal workstation  604  may take the form of a desktop computer with a display that shows graphical representations of the input and result data, and with a keyboard that enables the user to move files and execute processing software. LAN  605  provides high-speed communication between multi-processor computers  606  and with personal workstation  604 . The LAN  605  may take the form of an Ethernet network. 
     Multi-processor computer(s)  606  provide parallel processing capability to enable suitably prompt processing of the input data and measurement signals to derive the results data and control signals. Each computer  606  includes multiple processors  612 , distributed memory  614 , an internal bus  616 , a SAN interface  618 , and a LAN interface  620 . Each processor  612  operates on allocated tasks to solve a portion of the overall optimization problem and contribute to at least a portion of the overall results. Associated with each processor  612  is a distributed memory module  614  that stores application software and a working data set for the processor&#39;s use. Internal bus  616  provides inter-processor communication and communication to the SAN or LAN networks via the corresponding interfaces  618 ,  620 . Communication between processors in different computers  606  can be provided by LAN  605 . 
     SAN  608  provides high-speed access to shared storage devices  610 . The SAN  608  may take the form of, e.g., a Fibrechannel or Infiniband network. Shared storage units  610  may be large, stand-alone information storage units that employ magnetic disk media for nonvolatile data storage. To improve data access speed and reliability, the shared storage units  610  may be configured as a redundant disk array (“RAID”). 
     One or more cores  612  may make up a control module as shown in  FIG. 6C . The control module  650  may receive measurement signals from sensors  652  that monitor the oilfield operation  658 , and the control module  650  may send control signals to equipment  656  performing the oilfield operation  658 . The control module  650  may read from and write to a model of the oilfield operation stored in memory. In at least one embodiment, the control module includes cores and memory, and as such, the control module  650  itself includes the model  654 . The control module may be remotely coupled to the LAN  605  for communication purposes. 
       FIG. 7  shows an illustrative control module  650  for oilfield operation optimization. The control module  650  includes a long-term optimizer  702  coupled to one or more short-term optimizers  704 ,  706 . Each short-term optimizer  704 ,  706  controls one or more sub-processes  708 ,  710  of the oilfield operation, and measurement signals from the sub-processes  708 ,  710  are fed back into the long-term optimizer  702 , e.g. to update the operation model, and short-term optimizers  704 ,  706 , e.g. to update the actual job state. The long-term optimizer  702  constrains the short-term optimizers  704 ,  706 , e.g. with a desired job state, and the short term-optimizers  704 ,  706  control the oilfield operation within those constraints. 
     The long-term optimizer  702  maximizes a long-term cost function that includes one or more terms representing long-term rewards and one or more terms representing long-term risks. The long-term risks are static, or slow to change, over the job. Should a short-term risk persist over a threshold time period, the short-term risk may be promoted to a long-term risk. By maximizing the long-term cost function, the long-term optimizer  702  calculates an optimal design and passes the design to the short-term optimizers  704 ,  706 . The long-term cost function may be in the following form:
 
max {long-term rewards−costs+ρ·long-term risks}  (1)
 
where ρ is a weighting factor balancing the long-term risks. Equation (1) for a particular oilfield operation, e.g., increasing the stimulated reservoir volume (SRV) in a hydraulic fracturing environment, may take the following form:
 
max {SRV−material cost−supply chain cost+ρ·long-term risks}  (2)
 
subject to
 
     SRV=f 1  (DV) 
     material cost=f 2  (DV) 
     supply chain cost=f 3  (DV) 
     long-term risks=f 4  (DV) 
     where DV are decision variables. Similarly, equations may be formed for other oilfield operations including long-term rewards, costs, and long-term risks unique to those operations and discussed below. Due to the large number of decision variables, the long-term optimizer may only run every several hours or every several stages of the oilfield operation. The long-term optimizer may include one or more models that supplies the decision variables. For example, for a hydraulic fracturing operation, the models may include a Perkins-Kern-Nordgen model, a reservoir model, and a surface equipment model. The models only calculate final steady-state values of the variables and the intermediate responses are ignored in at least one embodiment. The inputs to the models may include measurement signals from the sub-processes  708 ,  710 . 
     The short-term optimizer modules  704 ,  706  minimize a short-term cost function according to constraints, the optimized decision variables, from the long-term optimizer  702 . The short term cost function may take the following form:
 
min {ρ 0·∥ J act −J des ∥ 2 +ρ 1 ·short-term rewards+ρ 2 ·short-term risks}  (3)
 
subject to
 
     J des =g 1  (DV optimal ) 
     J act =g 2  (job state) 
     short-term risks=g 3  (job state) 
     where the vector J des  contains the model design goals computed by the long-term optimizer and from the optimal long-term decision variables DV optimal , and where the vector J act  represents the current actual state of the job. ρ 0 , ρ 1  and ρ 2  are the weighting factors balancing the model-based control and risk-reward control. Short-term risks are quick-to-change risks that only exist in a local operational region over a short temporal window. The short-term optimizer modules  704 ,  706  may derive the current job state based on the measurement signals received and the control signals sent, and deriving the current job state may be performed with an adaptive system model. The control module  650  may allocate portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions. 
     After minimizing the short-term cost function, the short-term optimizers  704 ,  706  send control signals based on the minimization to the equipment performing the oilfield operation, and the short-term optimizers continue to receive measurement data regarding the operation from sensors. The control signals may automatically, i.e. without human input, adjust the equipment to balance risk and reward based on the short-term cost function as constrained by the long-term cost function. Measurement data is fed back to the short-term optimizers  704 ,  706  as well as the long-term optimizer  702  as measurement signals for updating the model, updating the job state, and the like. The short-term optimizers  704 ,  706  may also send data to the long-term optimizer  702  to adjust the model design goals. The long-term cost function may include a coordination term to balance the rewards and risks among several short-term optimizers  704 ,  706 . 
     The maximization of the long-term cost function can be computationally intensive, and as such, it may be computed less frequently than minimization of the short-term cost function, which may be computed continuously and in real time. 
     The model of the oilfield operation may take the form:
 
 x ( k+ 1)= Ax ( k )+ Bu ( k )+ w +( k )   (4)
 
 y ( k )= Cx ( k )+ v ( k )   (5)
 
where matrices A, B and C are the operation matrices and can be time-varying, and vector x(k) is the internal state of the operation. The vectors u(k) and y(k) are the input and output vectors of the operation, respectively. The process noise w(k) and measurement noise v(k) are considered to have a Gaussian distribution with covariance matrices W and V, respectively. The values of W and V can be from supplied from a user, learned from data, or measured directly by sensors. From Kalman filtering theory, the total uncertainty of the output y(k) can be represented by
 
Σ x ( k+ 1)= AΣ   x ( k ) A   T   +W−AΣ   x ( k ) C   T ( CΣ   x ( k ) C   T   +V ) −1   CΣ   x ( k ) A   T    (6)
 
Σ y ( k )= CΣ   x ( k ) C   T   +V ( k )   (7)
 
where Σx is the uncertainty matrix of state vector x(k), Σy is the uncertainty matrix of output y(k), and the diagonal elements of Σ y  are the uncertainty for individual decision variables. For example, in a hydraulic fracturing environment, the input vector may be u(k)=[F(k) c p (k)] T , where F(k) is the pump rate and c p (k) is the proppant concentration. The output vector may be y(k)=[L(k) w(k)] T , where L(k) and w(k) are fracture length and width, respectively. The first and second diagonal elements, Σ y,11  and Σ y,22 , of the uncertainty matrix Σy provide an estimate of current uncertainty of fracture dimensions.
 
     The weighting factors in the short-term optimizers  704 ,  706  may be adjusted dynamically according to the output uncertainty Σy. If the uncertainty level is low, for example if Σ y,11  and Σ y,22 , the diagonal elements of Σy, are smaller than 10% of the values of corresponding variables y 1  and y 2  (e.g., L(k) and w(k) in the example in the previous paragraph), the system may be operated in model-based control mode, which may be interpreted as a setpoint tracking objective, and thus the weighting factors are set as ρ 0 =1, ρ 1 =0 and ρ 2 =0. If the uncertainty level is high, for example if Σ y,11  and Σ y,22  are comparable with corresponding variables y 1  and y 2 , the system may be operated in risk-reward control mode, leading to ρ o =1, ρ 1 =1 and ρ 2 =1. If the uncertainty level is neither high nor low, for example if Σ y,11  and Σ y,22  are between 10% and 100% of corresponding variables y 1  and y 2 , then the system may be operated in a hybrid of model-based control mode and risk-reward control mode, and the weights may be determined by the uncertainty. For example, if Σ y,11 /y 1 =0.5 and Σ y,11 /y 1 =0.3, the average value of uncertainty-to-signal ratio is 0.4. Based on this value, the weighting factors may be chosen as ρ 0 =0.6, ρ 1 =0.4 and ρ 2 =0.4, meaning that 60% of the control effort is based on model-based control mode while 40% of the control effort is based on risk-reward control mode. 
     In hydraulic fracturing operations, the control signals may control or affect perforation density, borehole diameter, casing diameter, perforation diameter, fracturing fluid composition, proppant composition, gel breaker composition, pump rate, and proppant schedule. The long-term risks may include unsuitable locations for hydraulic fracturing, incompatibility between fracturing fluid and formation, and proppant screen out. The measurement signals of the hydraulic fracturing operation may include pressure and microseismic activity. 
     In drilling fluid operations, the control signals may affect or control pump rate, drilling fluid composition, fluid addition rate, rock cutting removal rate, and monitoring equivalent circulating density. The long-term risks may include equivalent circulating density below leak-off test and formation damage. Long-term rewards may include cost of drilling fluid and production rate of drilling fluid. 
     In well completion operations, short-term rewards may include including gravel-packing sand transport speed. Short-term risk may include thin gravel-packing carrier fluid and sand dune effect. Control signals may control or affect pump rate, gravel-packing sand concentration, and polymer composition. The measurement signals may include surface viscosity and pressure. The long-term risks may include reservoir damage and sand accumulation. The long-term rewards may include sand screening efficiency and borehole integrity. 
     In production chemical operations, the short-term rewards may include increase in production rate and increase in damage removal rate. The short-term risks may include downhole temperature change and reservoir fluid composition change. The control signals may control or affect the pump rate and chemical composition. The measurement signals may include separation of reservoir fluids, production rate, and damage removal rate. The long-term risks may include solid clogging. The long-term rewards may include separation capability of reservoir fluids, increase in production rate, and decrease in chemical cost. 
     In drilling operations, short-term rewards may include weight-on-bit and drillstring rotations-per-minute. The short-term risks may include a region of vibration. The long-term rewards may include, maximum dogleg severity, and rate of penetration. The control signals may control or affect the path taken by the bottomhole assembly. 
     In hydraulic workover operations, short-term rewards may include increased hydraulic workover pipe insertion speed. The short-term risks may include pressure release. The long-term risks may include incorrect hydraulic workover pipe location. The long-term rewards may include increased hydraulic workover pipe insertion speed and accurate hydraulic workover pipe location. The control signals may control or affect force of hydraulic workover pipe insertion, speed of hydraulic workover pipe insertion, type of hydraulic workover pipe, composition of hydraulic workover pipe, and diameter of hydraulic workover pipe. The measurement signals may include surface pressure and pipe end location. 
     In logging operations, short-term risks may include increased measurement noise, and biased measurements. The long-term risks may include inaccurate formation model and inaccurate reservoir model. The long-term rewards may include increased logging speed and increased logging resolution. The control signals may control or affect selection of logging tool and speed of logging tool. 
     In cementing operations, a short-term reward may include increased cement pumping rate. The short-term risks may include mud bubble, uneven cement surface, and poor cement bond. The long-term risks may include loss of cement integrity and low fracture gradient. The long-term rewards may include increased cement integrity, decreased wait-on-cement time, and decreased material cost. The control signals may control or affect cement type and cement composition. Measurement signals may include cement viscosity and cement pump rate. 
       FIG. 8  is a flow diagram of an illustrative method  800  of optimizing oilfield operations, beginning at  802  and ending at  814 . For clarity, the method  800  will be discussed using a hydraulic fracturing example, however, any oilfield operations in the above environments may be optimized using the optimization method  800 . At  804 , a formation-based model is analyzed to determine optimized values for a set of decision variables. For example, in a hydraulic fracturing operation the formation-based model may include a model of induced fractures, a model of a reservoir, and a model of surface equipment. The long-term cost function may be maximized for the long-term reward of stimulated reservoir volume (SRV), and as such the long-term optimizer may optimize the decision variables for proppant type, fluid type, gel type, and fracture plan (pump rate, proppant schedule, and the like) for each stage within the hydraulic fracturing job. The long-term risks used in the long-term cost function may be compatibility of fracturing fluid and formation and proppant screen-out. As such, the long-term optimizer may tend to select a cheaper fluid that is well-compatible with the formation and a proppant that is unlikely to screen out. At  806 , a desired job state is derived from the optimized values. The desired job state is passed as constraints to the short-term optimizers. 
     At  808 , measurement signals are obtained from an interface to equipment and sensors that perform the oilfield operation. These measurement signals are used to update the model of the oilfield operation and construct a current job state to be compared with the desired job state by the short-term optimizers. For a hydraulic fracturing operation, the measurement data may include pressure and microseismic activity measurements. At  810 , a current job state is derived based on the measurement signals. The short-term optimizer may then minimize the short term cost function based on a comparison between the desired job state and the current job state. Additionally, short-term risks and rewards are taken into account as well. At  812 , control signals are provided by the short-term optimizers that optimize the short-term cost function. For example, in a hydraulic fracturing operation, if the measurement data shows that pre-mature screen-out is likely to occur, then the short-term risk will increase and hence the short-term optimizer will try to reduce the short-term risk by sending a control signal to increase the flow rate. As such, the risk and reward analysis may change in real time according to changing conditions. 
       FIGS. 9A-9C  are diagrams of a specific risk and reward scenarios. Specifically, risk and reward scenarios for drilling operations are shown. In  FIG. 9A , a well path is shown that avoids a high-risk formation. Here, the long-term optimizer has constrained the short-term optimizers to avoid the high-risk formation, which poses a long-term risk to the drilling operation, but has left the method of avoidance (e.g. whether to drill above or below the high-risk region) to the short-term optimizers.  FIGS. 9B and 9C  illustrate short-term rewards and risks for the drilling operation. In terms of short-term rewards illustrated in  FIG. 9B , usually a higher weight-on-bit (WOB) and drillstring rotations-per-minute (RPM) lead to higher rate-of-penetration (ROP), which is the reward. However, as illustrated in  FIG. 9C  there exists some operating region where BHA vibration is very likely to happen. When uncertainties in both RPM and WOB are low, the measured values of RPM and WOB are close to their true values, thus the high risk region defined is small. If the uncertainties of both variables are high, the high-risk region will inflate to reduce the possibility of vibration occurring. Through continuous minimization of the short-term cost function, the short-term optimizer will find the optimal operating point for drilling. 
     A system for optimizing a hydraulic fracturing operation includes an interface to equipment and sensors for performing the hydraulic fracturing operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function including a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost. 
     The short-term optimizer module may derive the current job state based on the measurement signals and the control signals. Deriving the current job state may be performed with an adaptive system model. The system may allocate portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions. 
     A hydraulic fracturing operation optimization method includes analyzing a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function including at least a long-term reward and a final state cost. The method further includes deriving a desired job state from the optimized values. The method further includes obtaining measurement signals from an interface to equipment and sensors for performing the hydraulic fracturing operation. The method further includes deriving a current job state based at least in part on the measurement signals. The method further includes providing, to the interface, control signals that optimize a short-term cost function, the short-term cost function including a difference between the current job state and the desired job state, the control signals controlling one or more portions of the hydraulic fracturing operation selected from the group consisting of perforation density, borehole diameter, casing diameter, perforation diameter, fracturing fluid composition, proppant composition, gel breaker composition, pump rate, and proppant schedule. 
     A system for optimizing a drilling fluid operation includes an interface to equipment and sensors for performing the drilling fluid operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function including a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost. 
     The short-term cost function may include a short-term reward including rock chips removal rate. The short-term optimizer module may derive the current job state based on the measurement signals and the control signals. Deriving the current job state may be performed with an adaptive system model. 
     A drilling fluid operation optimization method includes analyzing a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function including at least a long-term reward and a final state cost. The method further includes deriving a desired job state from the optimized values. The method further includes obtaining measurement signals from an interface to equipment and sensors for performing the drilling fluid operation. The method further includes deriving a current job state based at least in part on the measurement signals. The method further includes providing, to the interface, control signals that optimize a short-term cost function, the short-term cost function including a difference between the current job state and the desired job state, the control signals controlling one or more portions of the drilling fluid operation selected from the group consisting of pump rate, drilling fluid composition, fluid addition rate, rock cutting removal rate, and monitoring equivalent circulating density. 
     A system for optimizing a well completion operation includes an interface to equipment and sensors for performing the well completion operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function including a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost. 
     The short-term cost function may include a short-term reward including gravel-packing sand transport speed. The short-term cost function may include a short-term risk created by the current job state selected from the group consisting of thin gravel-packing carrier fluid and sand dune effect. The short-term optimizer module may derive the current job state based on the measurement signals and the control signals. 
     A well completion operation optimization method includes analyzing a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function including at least a long-term reward and a final state cost. The method further includes deriving a desired job state from the optimized values. The method further includes obtaining measurement signals from an interface to equipment and sensors for performing the well completion operation. The method further includes deriving a current job state based at least in part on the measurement signals. The method further includes providing, to the interface, control signals that optimize a short-term cost function, the short-term cost function including a difference between the current job state and the desired job state, the control signals controlling one or more portions of the well completion operation selected from the group consisting of pump rate, gravel-packing sand concentration, and polymer composition. 
     A system for optimizing a production chemical operation includes an interface to equipment and sensors for performing the production chemical operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function including a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost. 
     The short-term cost function may include a short-term reward selected from the group consisting of increase in production rate and increase in damage removal rate. The short-term cost function may include a short-term risk created by the current job state selected from the group consisting of downhole temperature change and reservoir fluid composition change. The short-term optimizer module may derive the current job state based on the measurement signals and the control signals. 
     A production chemical operation optimization method includes analyzing a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function including at least a long-term reward and a final state cost. The method further includes deriving a desired job state from the optimized values. The method further includes obtaining measurement signals from an interface to equipment and sensors for performing the production chemical operation. The method further includes deriving a current job state based at least in part on the measurement signals. The method further includes providing, to the interface, control signals that optimize a short-term cost function, the short-term cost function including a difference between the current job state and the desired job state, the control signals controlling one or more portions of the production chemical operation selected from the group consisting of pump rate and chemical composition. 
     A system for optimizing a drilling operation includes an interface to equipment and sensors for performing the drilling operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function including a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost. 
     The short-term cost function may include a short-term reward selected from the group consisting of weight-on-bit and drillstring rotations-per-minute. The short-term cost function may include a short-term risk created by the current job state including a region of vibration. The short-term optimizer module may derive the current job state based on the measurement signals and the control signals. The current job state may be performed with an adaptive system model. The system may allocate portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions. The long-term cost function may include one or more long-term rewards selected from the group consisting of total length drilled, maximum dogleg severity, and rate of penetration. 
     A drilling operation optimization method includes analyzing a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function including at least a long-term reward and a final state cost. The method further includes deriving a desired job state from the optimized values. The method further includes obtaining measurement signals from an interface to equipment and sensors for performing the drilling operation. The method further includes deriving a current job state based at least in part on the measurement signals. The method further includes providing, to the interface, control signals that optimize a short-term cost function, the short-term cost function including a difference between the current job state and the desired job state, the control signals controlling a trajectory of a bottomhole assembly. 
     A system for optimizing a hydraulic workover operation includes an interface to equipment and sensors for performing the hydraulic workover operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function including a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost. 
     The short-term cost function may include a term representing a short-term reward including increased hydraulic workover pipe insertion speed. The short-term cost function may include a short-term risk created by the current job state comprising pressure release. The short-term optimizer module may derive the current job state based on the measurement signals and the control signals. Deriving the current job state may be performed with an adaptive system model. The system may allocate portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions. The long-term cost function may include long-term risks including incorrect hydraulic workover pipe location. The long-term cost function may include one or more long-term rewards selected from the group consisting of increased hydraulic workover pipe insertion speed and accurate hydraulic workover pipe location. 
     A hydraulic workover operation optimization method includes analyzing a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function including at least a long-term reward and a final state cost. The method further includes deriving a desired job state from the optimized values. The method further includes obtaining measurement signals from an interface to equipment and sensors for performing the hydraulic workover operation. The method further includes deriving a current job state based at least in part on the measurement signals. The method further includes providing, to the interface, control signals that optimize a short-term cost function, the short-term cost function including a difference between the current job state and the desired job state, the control signals controlling one or more portions of the hydraulic workover operation selected from the group consisting of force of hydraulic workover pipe insertion, speed of hydraulic workover pipe insertion, type of hydraulic workover pipe, composition of hydraulic workover pipe, and diameter of hydraulic workover pipe. 
     The long-term cost function may include one or more long-term rewards selected from the group consisting of increased hydraulic workover pipe insertion speed and accurate hydraulic workover pipe location. The long-term cost function may include long-term risk including incorrect hydraulic workover pipe location. The method may include allocating portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions. 
     A system for optimizing a logging operation includes an interface to equipment and sensors for performing the logging operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function including a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost. 
     The short-term cost function may include a short-term risk created by the current job state selected from the group consisting of increased measurement noise and biased measurements. The short-term optimizer module may derive the current job state based on the measurement signals and the control signals. Deriving the current job state may be performed with an adaptive system model. The system may allocate portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions. The long-term cost function may include long-term risks selected from the group consisting of inaccurate formation model and inaccurate reservoir model. The long-term cost function may include one or more long-term rewards selected from the group consisting of increased logging speed and increased logging resolution. 
     A logging operation optimization method includes analyzing a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function including at least a long-term reward and a final state cost. The method further includes deriving a desired job state from the optimized values. The method further includes obtaining measurement signals from an interface to equipment and sensors for performing the logging operation. The method further includes deriving a current job state based at least in part on the measurement signals. The method further includes providing, to the interface, control signals that optimize a short-term cost function, the short-term cost function including a difference between the current job state and the desired job state, the control signals controlling one or more portions of the logging operation selected from the group consisting of selection of logging tool and speed of logging tool. 
     A system for optimizing a cementing operation includes an interface to equipment and sensors for performing the cementing operation, wherein the interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer module that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function, the short-term cost function including a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost. 
     The short-term cost function may include a short-term reward including increased cement pumping rate. The short-term cost function may include a short-term risk created by the current job state selected from the group consisting of mud bubble, uneven cement surface, and poor cement bond. The short-term optimizer module may derive the current job state based on the measurement signals and the control signals. Deriving the current job state may be performed with an adaptive system model. The system may allocate portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions. The long-term cost function may include long-term risks selected from the group consisting of loss of cement integrity and low fracture gradient. The long-term cost function may include one or more long-term rewards selected from the group consisting of increased cement integrity, decreased wait-on-cement time, and decreased material cost. 
     A cementing operation optimization method includes analyzing a formation-based model to determine optimized values for a set of decision variables subject to a long-term cost function including at least a long-term reward and a final state cost. The method further includes deriving a desired job state from the optimized values. The method further includes obtaining measurement signals from an interface to equipment and sensors for performing the cementing operation. The method further includes deriving a current job state based at least in part on the measurement signals. The method further includes providing, to the interface, control signals that optimize a short-term cost function, the short-term cost function including a difference between the current job state and the desired job state, the control signals controlling one or more portions of the cementing operation selected from the group consisting of cement type and cement composition. 
     The long-term cost function may include long-term risks selected from the group consisting of loss of cement integrity and low fracture gradient. The long-term cost function may include one or more long-term rewards selected from the group consisting of increased cement integrity, decreased wait-on-cement time, and decreased material cost. The method of claim may include allocating portions of risk between the short-term cost function and the long-term cost function based on dynamic variability of those portions. 
     While the present disclosure has been described with respect to a limited number of embodiments, those skilled in the art will appreciate numerous modifications and variations therefrom. It is intended that the appended claims cover all such modifications and variations.