FIELD PRODUCTION STRATEGY OPTIMIZATION USING MULTI-OBJECTIVE GENETIC ALGORITHM

Systems and methods for operating wells of a field using a multi-objective genetic algorithm are disclosed. In one embodiment, a method of operating a plurality of wells within a field includes determining an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The method further includes operating the plurality of wells at the oil rate for each well.

BACKGROUND

In the oil and gas industry, it may be desirable to maximize production and recovery from hydrocarbon fields through the implementation of strategically designed field development plans and reservoir production strategies. Hydrocarbon fields include many, many wells, all of which should be controlled to meet certain requires of a production strategy. Therefore, wells are individually controlled to achieve the desired result. Calculation of production parameters for each well, such as oil production rate, is computationally expensive and also manually intensive.

Accordingly, alternative systems and methods for operating a plurality of wells of a hydrocarbon field are desired.

SUMMARY

Embodiments of the present disclosure are directed to systems and methods for operating a plurality of wells of a field, as well as systems and methods for determining oil rates for a plurality of wells of a field. A multi-objective genetic algorithm receives input data and determines one or more solution sets having an output that includes well product rates for the plurality of wells within the field. User-provided constraints define surface factors of the field, and user-provided global weight factors define a desired production strategy for the field.

In one embodiment, a method of operating a plurality of wells within a field includes determining an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The method further includes operating the plurality of wells at the oil rate for each well.

In another embodiment, a system for operating a plurality of wells within a field includes one or more processors, and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the one or more processors to determine an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The system further includes one or more well components of the plurality of wells, wherein the one or more well components are operated based on the oil rate for each well of the plurality of wells.

It is to be understood that both the foregoing general description and the following detailed description present embodiments that are intended to provide an overview or framework for understanding the nature and character of the claims. The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated into and constitute a part of this specification. The drawings illustrate various embodiments and together with the description serve to explain the principles and operation.

DETAILED DESCRIPTION OF THE DISCLOSURE

Embodiments of the present disclosure are directed to systems and methods for operating wells of a hydrocarbon field. In the oil and gas industry, a goal may be to achieve hydrocarbon production and recovery targets from hydrocarbon fields through the implementation of strategically designed field development plans and reservoir production strategies. In embodiments, under waterflooding schemes, reservoir strategies are designed to ensure a uniform movement of flood front while optimizing the reservoir pressure of the field and minimizing the production of water at the surface to achieve an oil production sustainably, prolonging the field's life and preventing excessive water production. To ensure that these strategies are implemented in the field, engineers are required to allocate production rates to individual wells and manually cross-validate these reservoir strategies while capturing all surface constraints in the field, such as the minimum and maximum rates of a production trunkline, train or a crude separation facility.

Typically, engineers must compile, check and analyze multiple reservoir parameters including: the well's locations, bottom-hole pressure, oil maximum potential rates, and water cut. These calculations are performed for each well to assign a production target that meets the overall reservoir strategy of the field. This task is usually computationally expensive because many individual calculations must be executed, as well as labor-intensive and time consuming. Thus, the task may take many days to complete. Additionally, this process, when done manually, may produce inconsistent results over the long run and is prone to human errors.

Embodiments of the present disclosure improve the computational efficiency of determining well production rates, and minimize manual involvement by use of a multi-objective genetic algorithm that automates the process, thereby ensuring the targeted production strategy is captured. Generally, embodiments provide systems and methods for receiving as input well data, and also global weight factors that define a production strategy and constraints defining characteristics of the field, such as trunkline and production facility characteristics. A multi-objective genetic algorithm receives the inputs and determines one or more solution sets having an output that includes well product rates for the plurality of wells within the field.

As stated above, the global weight factors are used to define a production strategy. Non limiting production strategies include a wet strategy, a dry strategy, and a mixed strategy. Referring now toFIG. 1, a wet production strategy102, a mixed production strategy104, and a dry production strategy106for a field are partially illustrated. In each strategy, a crest line CR is provided through the center of the field. The crest line CR may be generally aligned with the crest of the particular field. In the wet production strategy102, wells near the flank (i.e., the edges of the field) and furthest from crest line CR are opened and prioritized. InFIG. 1, wells that are opened are indicated by a non-shaded circle and wells that are shut in are indicated by a shaded circle. The dry production strategy 106 opens and prioritizes the wells closest to the crest line CR. In this strategy, production starts from the middle of the field and moves toward the flanks. The mixed production strategy104prioritizes wells near the flanks like the wet production strategy102but takes into consideration other factors, for example pressure, well location with respect to the crest of the reservoir, and water cut, and may therefore open wells close to the crest line CR.

Embodiments described herein automatically, and without user intervention, calculate optimum production rates for wells of the field in accordance with satisfying simultaneous objectives: honoring a target production rate for the field (i.e., meeting the target production rate within 90% or more), maximizing bottom-hole reservoir pressure, maximizing a distance of the wells to a crest line of the field, and minimizing a water cut of the field.

Referring now toFIG. 2, flowchart200illustrating a non-limiting example method for operating a plurality of wells is provided. It should be understood that the method may include more, fewer or different steps than as shown byFIG. 2.

At block202, input data regarding wells of the field are received by the system, which may be a computing device such as a desktop computer. The input data may be provided by any means. For example, a user may input the input data manually into the system, or the input data may be automatically read into the system. Non-limiting example input data includes wells' distance to the central up-structure location of the field (i.e., CR line), bottom hole pressure, wells' location, oil maximum potential rate, and water cut. Table 1 below shows non-limiting example input data.

The Wells column includes a well identifier, the Trunkline column includes a trunkline identifier, the Field column includes a field identifier, the Oil Rate column includes the oil rate for each well, the Water Rate column includes the water rate for each well, the WC column includes the water cut for each well, the X column includes the UTMX coordinate for each well, the Y column includes the UTMY coordinate for each well, the Pressure Intake column includes the pressure intake for each well, the SBHP column includes the static bottom-hole pressure for each well, the Crest column includes the distance of the well from the crest line CR, the Longitude column includes the longitude coordinate for each well, and the Latitude column includes the latitude coordinate for each well. It should be understood that embodiments are not limited to the input data provided by Table 1.

In some embodiments, at block204pre-processing is performed on the input data where all the input data are normalized between 0 and 1 to avoid any data bias. However, it should be understood that no pre-processing may be done in other embodiments.

Next, at block206, constraints and global weight factors are received by the system. In some embodiments, a user may enter the constraints into the system. Additionally or alternatively, constraints may be automatically downloaded into the system. The constraints define aspects of the field, and may be operational and/or non-linear. For example, surface constraints, such as trunkline maximum and minimum rates, maximum rates of a production facility, back pressure in the well head, ESP minimum operating rates and the like are defined as non-linear constraints in the system. The trunkline minimum rates are the minimum rate that hydrocarbons can flow through the respective trunklines. The maximum rate of a production facility is the maximum rate of hydrocarbons that can be processed by the production facility. Other operating conditions may be specified, such as the minimum operating bottom-hole pressure for each well.

Global weight factors may also be received by the system. As described in more detail below, the global weight factors are introduced into the multi-objective fitness function and can be inputted by the user to define the targeted strategy.

Next, at block208, the input data, constraints, and global weight factors are provided to the multi-objective genetic algorithm. The multi-objective genetic algorithm comprises a multi-objective fitness function that is defined by two objective functions yi and yz, non-limiting examples of which are provided below.

χ1: lower and upper bound of the decision variable of the multi-objective genetic algorithm, representing the well's choke size,

qTarget: the oil target rate of the field,

qoi: the maximum potential oil rate per well,

N: total number of wells in the field,

Pnormi: normalized reservoir pressure per well,

dnormi: normalized well's distance to the crestal line of the field,

WCnormi: normalized water cut per well,

a1: global weight factor for the normalized pressure variable,

a2: global weight factor for the normalized distance variable, and

a3: global weight factor for the normalized water cut variable

The first fitness function y1honors a target oil rate for the field, and the second objective function y2maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field.

As stated above, the global weight factors are introduced into the objective function and define a production strategy that can be optimized by adjusting these global weight factors. For example, to generate a wet production strategy with a uniform production from flank to crest of the field, the pressure and the water cut weights, a1and a3, respectively, are set to 0. The multi-objective genetic algorithm will only use the distance to allocate the individual well's target and will produce the field from flank to crest. For a dry production strategy, al and az should be set to zero because the field will be produced based on the water rate, where wells with high water cut will be restricted by the multi-objective genetic algorithm. For a mixed production strategy, the weights are set for all factors based on a pre-knowledge of the reservoir information and heterogeneity. Multiple sensitivity tests may be run to determine the weights. Each result may be compared with a target rate that was generated by engineers previously. By setting the correct weights, the multi-objective genetic algorithm will produce the field not only based on location, but also look at other variables including, without limitation: pressure, water cut and distance to produce the optimal production strategy. Furthermore, other localized weights can also be introduced per selected group(s) of wells. For example, a local pressure and distance weight factor can be defined per group based on the wells' performance and knowledge of pressure and distance distribution before applying the global weight factors.

The multi-objective fitness function is defined to honor the field's target rate (y1), maximize the bottom-hole reservoir pressure, maximize the distance to the crest line location of the field and minimize the field's overall water cut (y2). The multi-objective genetic algorithm generates initial candidate solutions that are tested against the multi-objective fitness function. As an example and not a limitation, the multi-objective genetic algorithm may be executed using the gamultiobj function in MATLAB provided by MathWorks of Natick, Mass. The multi-objective genetic algorithm will produce a new generation of solutions to search for best candidates by applying multiple genetic algorithm processes involving selection, cross-over and mutation. The multi-objective genetic algorithm captures all the facility constraints defined by the user. For example, wells with an oil rate less than 800 bbl of oil and water cut higher than 80% will be closed, the minimum rate per trunk line is 20 MBD and the GOSP minimum operation rate is 110 MBD.

Based on this genetic process, optimum scenarios are selected by the multi-objective genetic algorithm achieving the multi-objective genetic algorithm's objectives and meeting the multi-objective fitness function. At block 210, it is determined whether or not the multi-objective fitness function termination criterion are satisfied. That is, it is determined whether or not the target rate is honored, the bottom-hole reservoir pressure is maximized, a distance of the wells to a crest line of the field is maximized, and a water cut of the field is minimized. If not, the process produces another generation of solutions are generated and evaluated by moving back to block208and continuing again to block210.

Once the multi-objective fitness function termination criterion are satisfied at block210, the process moves to block212, where a set of solutions is outputted. The set of solutions includes the oil rate for each well within the field. These oil rates are then applied to the individual wells such that the wells are operated according to the assigned oil rates. For example, well components890(seeFIG. 8), such as a wellhead choke, are operated so that the wells operate at the desired oil rates.

Accordingly, embodiments enable quick and efficient calculation of oil rates to effectuate a production strategy for a field that significantly reduces the amount of computing power and human time required by traditional methods.

Referring now toFIG. 3, a graph302illustrating trunkline oil rate (i.e., oil velocity) as a percentage of a total oil rate for six example trunklines TL-1—TL6, and how each trunkline is above a minimum oil rate as defined by a user, which in this case is 30% of a total oil rate of the particular trunklines. The minimum trunkline oil rate is a non-linear constraint selected by the user. As shown by the graph302, the oil rates of the wells outputted by the multi-objective genetic algorithm ensure that the non-linear constraint of the minimum trunkline oil rate is satisfied.

FIG. 4is a graph402illustrating production rates for groups of wells per oil rates determined by the multi-objective genetic algorithm under a mixed production strategy example. The multi-objective genetic algorithm provided a uniform flood front from flank to crest of the field. The wells were grouped in rows, wherein the 1stgroup includes the wells closest to the flank of the field, followed by the 2nd, 3rd, 4thand 5thgroup of wells, which are increasingly further from the flank of the field. The Y-axis of the graph402is the percentage of wells producing oil in each group. Thus, the percentage of each bar of the graph402represents the optimized production obtained from the multi-objective genetic algorithm over the total production potential of the particular production row. The multi-objective genetic algorithm will produce evenly from all wells in the field to ensure a uniform production from flank to crest. In keeping with the mixed production strategy, the wells of the 5thgroup, which are furthest from the flank, produce less than the wells of the other groups.

FIG. 5illustrates a mixed production strategy502of a field. The well labeled Well-A has a lower pressure and is located further updip as compared to the well labeled Well-B. As such, the output of the multi-objective genetic algorithm will automatically choke Well-A more than Well-B.

FIGS. 6 and 7show the normalized distribution of oil rates versus well count from both the multi-objective genetic algorithm and originally inputted oil rate potential for wells within the field according to a mixed production strategy, respectively. Particularly,FIG. 6is a histogram602showing the number of wells producing at the oil rate (in 1,000 barrels per day) within the bins along the X-axis as determined by the multi-objective genetic algorithm. For example, there are about 60 wells producing less than 1,000 barrels per day, about 160 wells producing between 1,000 barrels per day and 2,000 barrels per day, and about 60 wells producing between 3,000 and 4,000 barrels per day.

FIG. 7is a histogram702similar to the histogram602ofFIG. 6except it shows potential oil production rather than production as provided by the multi-objective genetic algorithm. According to the histogram, for example, there are about 40 wells having a potential of producing less than 1,000 barrels per day, about 80 wells having a potential of producing between 1,000 and 2,000 barrels per day, and about 110 wells having a potential of producing between 3,000 and 4,000 barrels per day. In comparing the histogram702ofFIG. 7with the histogram602ofFIG. 6, it is shown that the multi-objective genetic algorithm decreases the potential oil rate of wells within the field to satisfy the multi-objective fitness function while considering the user-defined constraints. For example, a number of wells capable of producing oil at a rate of more than 3,000 barrels per day have their output rate significantly reduced according to the multi-objective genetic algorithm as shown in the histogram602ofFIG. 7.

Embodiments of the present disclosure may be implemented by a computing device, and may be embodied as computer-readable instructions stored on a non-transitory memory device.FIG. 8depicts an example computing device802configured to perform the functionalities described herein. The example computing device802provides a system for determining oil rates for wells of a field as well as operating wells of a field, and/or a non-transitory computer usable medium having computer readable program code for determining oil rates for wells of a field as well as operating wells of a field embodied as hardware, software, and/or firmware, according to embodiments shown and described herein. While in some embodiments, the computing device802may be configured as a general purpose computer with the requisite hardware, software, and/or firmware, in some embodiments, the computing device802may be configured as a special purpose computer designed specifically for performing the functionality described herein. It should be understood that the software, hardware, and/or firmware components depicted inFIG. 8may also be provided in other computing devices external to the computing device802(e.g., data storage devices, remote server computing devices, and the like).

As also illustrated inFIG. 8, the computing device802(or other additional computing devices) may include a processor830, input/output hardware832, network interface hardware834, a data storage component836(which may store well data838A, weight and constraint data838B, and any other data838C), and a non-transitory memory component840. The memory component840may be configured as volatile and/or nonvolatile computer readable medium and, as such, may include random access memory (including SRAM, DRAM, and/or other types of random access memory), flash memory, registers, compact discs (CD), digital versatile discs (DVD), and/or other types of storage components. Additionally, the memory component840may be configured to store operating logic841and multi-objective genetic logic842(each of which may be embodied as computer readable program code, firmware, or hardware, as an example). A local interface846is also included inFIG. 8and may be implemented as a bus or other interface to facilitate communication among the components of the computing device802.

The processor830may include any processing component configured to receive and execute computer readable code instructions (such as from the data storage component836and/or memory component840). The input/output hardware832may include an electronic display device, keyboard, mouse, printer, camera, microphone, speaker, touch-screen, and/or other device for receiving, sending, and/or presenting data. The network interface hardware834may include any wired or wireless networking hardware, such as a modem, LAN port, wireless fidelity (Wi-Fi) card, WiMax card, mobile communications hardware, and/or other hardware for communicating with other networks and/or devices, such as external devices for operating well components890(e.g., valves).

It should be understood that the data storage component836may reside local to and/or remote from the computing device802, and may be configured to store one or more pieces of data for access by the computing device802and/or other components. As illustrated inFIG. 8, the data storage component836may include well data838A, which in at least one embodiment includes well input data provided by a user or automatically acquired through various means. Similarly, weight and constraint data838B may be stored by the data storage component836and may include data relating the global weight factors and constraints provided by the user. Other data838C to perform the functionalities described herein may also be stored in the data storage component836.

Included in the memory component840may be the operating logic841and the multi-objective genetic algorithm logic842. The operating logic841may include an operating system and/or other software for managing components of the computing device802. Similarly, multi-objective genetic algorithm logic842may reside in the memory component840and is configured to determine the oil rates in accordance with the global weight factors and constraints provided by the user.

It should now be understood that embodiments of the present disclosure are directed to systems and methods for determining oil rates for wells of a field, as well as systems and methods for operating wells of a field. In embodiments, reservoir strategies are automatically generated that honor a field's production target and provide a uniform movement of flood front while optimizing the reservoir pressure of the field and minimizing the production of water at the surface to achieve an oil production sustainably, prolonging the field's life and preventing excessive water production. Particularly, embodiments of the present disclosure improve the computational efficiency of determining well production rates, and minimize manual involvement by use of a multi-objective genetic algorithm that automates the process of determining oil rates of wells within the field, thereby ensuring the targeted production strategy is captured. Embodiments disclosed herein receive as input well data, and also global weight factors that define a production strategy and constraints defining characteristics of the field, such as trunkline and production facility characteristics. A multi-objective genetic algorithm receives the inputs and determines one or more solution sets having an output that includes well product rates for the plurality of wells within the field. These oil rates may then be automatically provided to the wells so that components of the wells are operated to achieve the desired oil rate.

In a first aspect of the disclosure, a method of operating a plurality of wells within a field includes determining an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The method further includes operating the plurality of wells at the oil rate for each well.

In a second aspect, a method according to the first aspect, wherein the multi-objective fitness function is iteratively executed until the multi-objective fitness function is satisfied.

In a third aspect, a method according to the first aspect or the second aspect, the multi-objective genetic algorithm produces a plurality of solution generations by applying selection, cross-over and mutation.

In a fourth aspect, a method according to any preceding aspect, further including receiving input data into the multi-objective genetic algorithm, and receiving one or more constraints into the multi-objective genetic algorithm.

In a fifth aspect, a method according to the fourth aspect, wherein the input data includes for each well of the plurality of wells, one or more of well coordinates, water rate, maximum oil rate, well structure depth, bottom-hole pressure and water cut.

In a sixth aspect, a method according to the fourth aspect or the fifth aspect, wherein the one or more constraints include one or more operation conditions including minimum operating bottom-hole pressure, and one or more non-linear constraints including one or more of minimum trunkline rate, maximum facility production rate, minimum group production rate, and maximum group production rate.

In a seventh aspect, a method according to any preceding aspect, further comprising applying one or more global weight factors to the multi-objective genetic algorithm to define a production strategy.

In an eighth aspect, a method according to the seventh aspect, wherein the production strategy is selected from a wet production strategy, a dry production strategy, and a mixed production strategy.

In a ninth aspect, a method according to the seventh or eighth aspect, wherein the one or more global weight factors comprise a pressure weight factor, a distance weight factor, and a water cut weight factor.

In a tenth aspect, a method according to any preceding aspect, wherein the oil rate for each well of the plurality of wells is such that the field produces a uniform flood front from flank to crest.

In an eleventh aspect, a system for operating a plurality of wells within a field includes one or more processors, and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the one or more processors to determine an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The system further includes one or more well components of the plurality of wells, wherein the one or more well components are operated based on the oil rate for each well of the plurality of wells.

In a twelfth aspect, a system according to the eleventh aspect, wherein the multi-objective fitness function is iteratively executed until the multi-objective fitness function is satisfied.

In a thirteenth aspect, a system according to the eleventh or twelfth aspect, wherein the multi-objective genetic algorithm produces a plurality of solution generations by applying selection, cross-over and mutation.

In a fourteenth aspect, a system according to any one of the eleventh through thirteenth aspects, wherein the instructions further cause the one or more processors to receive input data into the multi-objective genetic algorithm, and receive one or more constraints into the multi-objective genetic algorithm.

In a fifteenth aspect, a system according to the fourteenth aspect, wherein the input data includes for each well of the plurality of wells, one or more of well coordinates, water rate, maximum oil rate, well structure depth, bottom-hole pressure and water cut.

In a sixteenth aspect, a system according to the fourteenth or fifteenth aspect, wherein the one or more constraints comprise one or more operating constraints comprising minimum operating bottom-hole pressure, and one or more non-linear constraints comprising one or more of minimum trunkline rate, maximum facility production rate, minimum group production rate, and maximum group production rate.

In a seventeenth aspect, a system according to any one of the eleventh through sixteenth aspects, wherein the instructions further cause the one or more processors to apply one or more global weight factors to the multi-objective genetic algorithm to define a production strategy.

In an eighteenth aspect, a system according to the seventeenth aspect, wherein the production strategy is selected from a wet production strategy, a dry production strategy, and a mixed production strategy.

In a nineteenth aspect, a system according to the seventeenth or eighteenth aspect, wherein the one or more global weight factors comprise a pressure weight factor, a distance weight factor, and a water cut weight factor.

In a twentieth aspect, a system according to any one of the eleventh through nineteenth aspects, wherein the oil rate for each well of the plurality of wells is such that the field produces a uniform flood front from flank to crest.