Patent Publication Number: US-10324230-B2

Title: Integrated interpretation of pressure and rate transients for production forecasting

Description:
BACKGROUND 
     This disclosure relates to methods and systems for well testing, and more particularly to a system and method for well test interpretation and a combination of analysis techniques with other earth science data. 
     DESCRIPTION OF THE RELATED ART 
     In any subsurface exploration and development, indirect measurements such as detailed geological description, outcrop data, etc., and direct measurements such as seismic, cores, logs, and fluid samples, etc., provide useful information for static and dynamic reservoir description, simulation, and forecasting. However, core and log data delineate rock properties just in the vicinity of the wellbore while geological and seismic data often are not directly related to formation permeability. Pressure transient tests provide dynamic information about reservoir pressure which can be used to estimate rock properties and fluid distributions and fluid samples for well productivity and dynamic reservoir description. Therefore, such tests are very useful for exploration environments as well as for general production and reservoir engineering. 
     SUMMARY 
     Certain aspects of some embodiments disclosed herein are set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of certain forms the invention might take and that these aspects are not intended to limit the scope of the invention. Indeed, the invention may encompass a variety of aspects that may not be set forth below. 
     In one embodiment of the present disclosure, a method for well production forecasting using a geological reservoir model and measured transient flow rate and pressure data includes calibrating the geological reservoir model. This calibrating includes analyzing a series of pressure transient well tests providing measured transient flow rate and pressure data for a well. The calibrating further includes fitting the geological reservoir model to the measured transient pressure data using the measured transient flow rate as input to estimate a first set of parameters for the geological reservoir model, and then building a simulated history of bottom hole pressure for the well based on the geological reservoir model and the measured transient flow rate data. Also, the calibrating includes fitting the geological reservoir model to the measured transient flow rate data using the measured transient pressure data and the simulated history of bottom hole pressure as input to estimate a second set of parameters for the geological reservoir model, followed by building a simulated history of downhole flow rate for the well based on the geological reservoir model and change in the measured transient pressure data. The method also includes forecasting future production from the well using the calibrated geological reservoir model. 
     In another embodiment of the present disclosure, an apparatus includes a programmed computer for analyzing a series of pressure build-up and/or drawdown well tests for a well to a given time using a measured rate history. The computer is also programmed to determine a system type of a geological reservoir model, to determine model parameters for the geological reservoir model using a maximum likelihood estimator, and to build a history of the well bottom hole pressure using a pressure convolution equation. Further, the computer is programmed to analyze the series of pressure build-up and/or drawdown well tests to the given time using pressure data as input to determine the system type of the geological reservoir model, and to determine model parameters for the geological reservoir model using the maximum likelihood estimator with measured flow rates a target of the maximum likelihood estimator. The pressure data used as input includes pressures measured during the pressure build-up and/or drawdown well tests, as well as simulated pressures from the history of the well bottom hole pressure built using the pressure convolution equation. Further still, the computer is programmed to build a history of the well downhole flow rate using a rate convolution equation, and to forecast future well production using the geological reservoir model. 
     In another embodiment, a method for modelling a production forecast for a well using a geological reservoir model includes analyzing a series of pressure build-up and/or drawdown well tests to a given time using a measured rate history, determining a system type of the geological reservoir model, determining model parameters for the geological reservoir model using a maximum likelihood estimator, and building a history of the well bottom hole pressure using a pressure convolution equation. The method also includes analyzing the series of pressure build-up and/or drawdown well tests to the given time using pressure data as input to determine the system type of the well, and determining model parameters for the geological reservoir model using the maximum likelihood estimator with measured flow rates a target of the maximum likelihood estimator. The pressure data used as input includes both pressures measured during the pressure build-up and/or drawdown well tests and simulated pressures from the history of the well bottom hole pressure built using the pressure convolution equation. The method further includes building a history of the well downhole flow rate using a rate convolution equation. Additionally, the method includes using the geological reservoir model to forecast future production from the well. 
     Various refinements of the features noted above may exist in relation to various aspects of the present embodiments. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. Again, the brief summary presented above is intended just to familiarize the reader with certain aspects and contexts of some embodiments without limitation to the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Certain embodiments of the inventive concepts will hereafter be described with reference to the accompanying drawings, wherein like reference numerals denote like elements. It should be understood, however, that the accompanying figures illustrate the various implementations described herein and are not meant to limit the scope of the various technologies described herein. 
         FIG. 1  shows a schematic diagram of a method for modelling a reservoir in accordance with the present disclosure. 
         FIG. 2  is a flowchart representative of a process for forecasting future production from a well according to some embodiments. 
         FIG. 3  shows a diagram of pressure and rate of a well over time using a naturally fractured reservoir model according to some embodiments. 
         FIG. 4  shows a diagram of pressure and rate of a well over time using a dual-porosity pseudo-steady-state reservoir model according to some embodiments. 
         FIG. 5  is a flowchart representative of a process for well production forecasting using a geological reservoir model in accordance with some embodiments. 
         FIG. 6  is a block diagram generally depicting components of a programmed computer system for modelling a reservoir and forecasting future production in accordance with some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Specific embodiments of the inventive concepts disclosed herein will now be described in detail with reference to the accompanying drawings. Further, in the following detailed description of embodiments of the present disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the embodiments disclosed herein may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description. 
     When introducing elements of various embodiments, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Moreover, any use of “top,” “bottom,” “above,” “below,” other directional terms, and variations of these terms is made for convenience, but does not mandate any particular orientation of the components. 
     In the following description, numerous details are set forth to provide an understanding of the present disclosure. However, it will be understood by those skilled in the art that the embodiments of the present disclosure may be practiced without these details and that numerous variations or modifications from the described embodiments may be possible. 
     This disclosure describes a new method of improving the confidence in the parameters and description of a Reservoir Model derived from Well Test Transient Analysis. The method includes performing a conventional Pressure Transient Analysis (PTA) and identifying the model and a set of parameters (permeability, location of boundaries, fracture size and distribution, etc.) that most closely matches the measured pressure data. Typically a Pressure Buildup analysis is performed due to the confidence in the measured rate (identically zero). In this analysis the measured rate data is imposed as a boundary condition on the well over time. In other words it is the change in rate at the well head that induces a pressure disturbance in the reservoir. 
     In accordance with at least some embodiments, this method also includes using the downhole pressure as the boundary condition on the well, in which it is the pressure changes at the perforations that are deemed responsible for the disturbance moving through the reservoir. This technique, which may be called Rate Transient Analysis (RTA), uses the measured pressure data to produce simulated rate-versus-time data that is adjusted during system identification of a modelling process (e.g., the process described below with respect to  FIG. 1 ) to match the measured rate data. In at least some instances, the PTA and RTA techniques are run iteratively until their results agree to produce a more robust and accurate reservoir model and associated parameters. The model with these more accurate parameters is then used for production prediction. 
       FIG. 1  illustrates a general overview of some methods according to embodiments described herein that may be used to gather, model, analyze, and interpret the data measured during well tests (e.g., pressure transient tests and production tests) along with other types data to determine a final geological reservoir model and formation properties. More specifically,  FIG. 1  is a diagram  20  that generally represents a procedure for collecting pressure and rate data from a well with a data acquisition system (block  22 ) and using it to extract the values of reservoir parameters of interest. Due to the non-linear nature of the physical processes (e.g., complex flow in the wellbore, complex rock-fluid interaction, and uncertainties about the reservoir geology), this can be an iterative procedure in which initial guesses at the parameter values are refined. 
     Various data can be processed (block  24 ), such as pressure and flow rate data (which can be provided as log-log plots), and used for system identification (blocks  26  and  28 ). This system identification can include identifying reservoir or formation features, such as a particular flow regime (linear, radial, bounded, wellbore storage dominated, etc.), and estimating well and reservoir parameters. In at least some embodiments, and as generally depicted in  FIG. 1 , PTA and RTA techniques may be used together, and system identification can be performed with pressure imposed as a boundary condition (block  26 ) and with rate imposed as a boundary condition (block  28 ). Once the system characteristics are understood, parameters dominating the flow during each regime can be inferred and refined. Use can be made of regression software to alter the parameter values to most closely fit the data (blocks  30 ,  32 ,  34 ). 
     Conventional well tests have traditionally been used to obtain spatial distributions of the formation permeability, pressure, and other well and reservoir parameters based on the history matching of the pressure data to an analytical or simple numerical model selected to most closely represent the flow regimes observed from diagnostic plots. 
     Similarly, tests can be performed (often in the production environment) where the permeability and pressure in the formation is based on the automatic history matching of the rate data. Although these approaches may be mathematically similar, the metrology of the measurement devices and the fluctuations of the measurements with time differ and the system identification technique and its parameters may differ. This disclosure proposes a technique to reconcile these differences by converging on a maximum likelihood model. 
     Pressure derivative can be used as a diagnostics plot in PTA. During model match (of system identification), either pressure data or its derivative can be used to identify system properties. The same approach can be applied to rate derivative as well. In some cases, rate derivative and pressure derivative can exhibit different behaviors or can indicate the effect of certain reservoir properties at different times. In accordance with at least one embodiment, the actual data (pressure and rate) or their derivatives are automatically selected for model matching purposes based on the match quality (e.g., according to an objective function). 
     The type curve fitting used in PTA and material balance modelling of RTA is not ideal since they do not account for the presence of complex geology and fluid rock interaction that is present in actual reservoirs. These inadequacies have been addressed by introducing real geological models and reservoir simulators into the system identification of  FIG. 1 . Pressure Transient Analysis may be constrained by the test design to focus on specific reservoir zones of interest with the well flowing under ideal conditions (e.g., remaining above the bubble point) and for a short flowing time. The application of similar techniques to Rate Transient Analysis is not trivial, as these constraints are not present for RTA. RTA may have to account for many wells flowing multiple phases from many producing zones and this production data has a much longer duration than the transient pressure test. Consequently, there are many more reservoir parameters to be accounted for, both spatially and in terms of fluid saturations, and their nonlinear interaction can be accommodated during the system identification process. 
     Additionally, in complex reservoirs containing natural fractured systems, the PTA approach can be used to calibrate near-wellbore fracture parameters (conductivity, length, density etc.). During RTA, it is the interaction between the larger-scale networks of fractures, their orientation, and macro-scale features that become more meaningful. In accordance with some embodiments, these multiple scales are recognized and the iterative procedure outlined in this disclosure can be used to recover more information from pressure and rate measurements during modelling. 
     In some cases, the grid block properties of the geological model can be modified in such a way as to improve the match between the simulation and measured pressure data. In some embodiments, this functionality is extended to include the rate measurements. In common with the PTA approach of some modelling systems, the RTA takes into account a “Prior Model” which for RTA is the latest up-scaled full-field history match together with the original geological model. To achieve the meaningful combination of PTA and RTA in one process, at least some embodiments use a reservoir model that runs efficiently at different time and length scales and where the reservoir properties updated in one transient analysis technique inform the properties of the other scale through efficient downscaling or upscaling, which may be facilitated by the gridding process generally noted above. 
     In addition to the multiple spatial scales of measurements, there tends to be meaningful differences in the standard deviation in the measured transient rate and pressure responses during a well test. Embodiments in accordance with the present disclosure may use both types of measured data (pressure and rate) along with their error estimates to produce system identification along with associated reservoir parameters. This iterative identification (shown in  FIG. 1 ) exploits the different sensitivity of the underlying model parameters to the forward (PTA) and backward (RTA) analysis. 
     A mathematical algorithm used during the inversion process is a maximum likelihood estimator rather than the more typical method of least squares regression in at least some embodiments. Additionally this new approach can be applied sequentially to the measured pressure and rate data. For example, in accordance with certain embodiments, a method includes the following:
         1) Analyzing a series of pressure build-up and/or drawdown tests to a given time using the measured rate history.   2) Determining the system, and determining model parameters using a maximum likelihood estimator (MLE).   3) Building the full history of the well bottom hole pressure using a pressure convolution equation:       

     
       
         
           
             
               
                 
                   
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             where p u (t) is the unit rate impulse response of the well and reservoir model, p(t) is the simulated (output) pressure as a function of time t, and q(t) is the measured rate as a function of time t. Note that either form of the convolution integral can be used as convenient. 
             4) Repeating 1) and 2) using the pressure data as input with the measured rates the target of the MLE. 
             5) Building the full history of the well downhole rate using a rate convolution equation: 
           
         
       
    
     
       
         
           
             
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             where q u (t) is the unit pressure change impulse response of the well and reservoir model, q(t) is the simulated (output) rate as a function of time t, and Δp(t) is the measured change in pressure (e.g., with respect to an initial pressure) as a function of time. 
             6) Repeating 1)-5) for a later series of pressure build-ups and/or drawdowns. 
             7) Comparing the models and parameters to determine if the well or reservoir model is changing with time. 
           
         
       
    
     In accordance with the above description, one example of a method for modelling a well production forecast using a geological reservoir model is generally represented by flowchart  40  in  FIG. 2 . The method can include analyzing pressure transient well tests to a given time using a measured rate history as input (block  42 ) and determining a system type of the geological reservoir model and model parameters (block  44 ). The pressure transient well tests may include a series of pressure build-up and/or drawdown well tests, and determining the system type can include determining a flow regime of the well. In one embodiment, geological reservoir model parameters are determined using a maximum likelihood estimator. The method further includes building a history of bottom hole pressure for the well (block  46 ). This can be done using a suitable pressure convolution equation, such as those described above. Examples of the constructed history of bottom hole pressure are shown in the upper plots of  FIGS. 3 and 4 . 
     Additionally, the method includes analyzing the pressure transient well tests using pressure data (block  48 ) as input to the model to determine the system type of the geological reservoir model and to determine model parameters. The model parameters may be determined using the maximum likelihood estimator with measured flow rates as a target of the maximum likelihood estimator. Further, the pressure data used as input can include both measured pressures (e.g., measured during the pressure transient well tests) and simulated pressures (e.g., from the built history of the well bottom hole pressure noted above). A history of the well downhole flow rate can also be built (block  50 ). This downhole flow rate history can be built with a rate convolution equation, as described above. Examples of such a downhole flow rate history are shown in the lower plots of  FIGS. 3 and 4 . 
     Still further, the method includes using the geological reservoir model to forecast future production from the well (block  52 ). More specifically, this more detailed model and its parameters can be used to predict the rate response of the well into the future, i.e., production forecasting (see, e.g.,  FIG. 3 ). In at least some instances, the method may be repeated based on later testing (e.g., for a subsequent series of pressure transient tests) and the results can be compared to previous results to determine whether the well or reservoir model is changing with time. In one embodiment, pressures and flow rates measured during a later series of pressure build-up and/or drawdown well tests are compared to pressures and flow rates predicted by the geological reservoir model to assess predictive accuracy of the model. In at least some instances, the results of the workflow described above and the incorporation of any time dependency in the model parameters provide increased confidence in the production forecast. The presently disclosed techniques can be used with various reservoirs, but may be particularly suited to low-permeability reservoirs where the transient response of the well can last for years. 
     Another example of a method for well production forecasting in accordance with the description above is generally represented by flowchart  60  in  FIG. 5 . This method generally includes calibrating a geological reservoir model, which itself includes analyzing pressure transient well tests (block  62 ) providing measured transient flow rate and pressure data for a well, such as described above. The calibration also includes fitting the geological reservoir model to the measured transient pressure data, using the measured transient flow rate as input, to estimate certain parameters of the model (block  64 ). A simulated history of bottom hole pressure for the well is built based on the geological reservoir model and the measured transient flow rate data (block  66 ). 
     The calibration also includes using the measured transient pressure data and the simulated history of bottom hole pressure as input to fit the geological reservoir model to the measured flow rate data to estimate parameters of the model (block  68 ). The parameters estimated in this manner may include at least some of the parameters estimated by fitting the model to the measured pressure data, but may also include additional parameters not quantified in the prior estimation. The calibration may continue with building a simulated history of the downhole flow rate for the well based on the geological reservoir model and a change in the measured transient pressure data (block  70 ), such as described above. Future well production may be forecast using the calibrated geological reservoir model (block  72 ). As noted above, additional data may be used to update the model and to determine whether the model or the well are changing over time. In one embodiment, the additional data includes flow rates measured at certain periods with a series of production well tests. 
     It will be appreciated that a computer system can be programmed to facilitate performance of the above-described methods for modelling and production forecasting. An example of such a computer system is generally depicted in  FIG. 6  in accordance with one embodiment. In this example, the computer system  80  includes a processor  82  connected via a bus  84  to volatile memory  86  (e.g., random-access memory) and non-volatile memory  88  (e.g., flash memory and a read-only memory (ROM)). Coded application instructions  90  and data  92  are stored in the non-volatile memory  88 . For example, the application instructions  90  can be stored in a ROM and the data  92  can be stored in a flash memory. The instructions  90  and the data  92  may also be loaded into the volatile memory  86  (or in a local memory  94  of the processor) as desired, such as to reduce latency and increase operating efficiency of the computer system  80 . The coded application instructions  90  can be provided as software that may be executed by the processor  82  to enable various functionalities described herein. Non-limiting examples of these functionalities include the modelling and well production forecasting described above. In at least some embodiments, the application instructions  90  are encoded in a non-transitory computer readable storage medium, such as the volatile memory  86 , the non-volatile memory  88 , the local memory  94 , or a portable storage device (e.g., a flash drive or a compact disc). 
     An interface  96  of the computer system  80  enables communication between the processor  82  and various input devices  98  and output devices  100 . The interface  96  can include any suitable device that enables this communication, such as a modem or a serial port. In some embodiments, the input devices  98  include a keyboard and a mouse to facilitate user interaction and the output devices  100  include displays, printers, and storage devices that allow output of data received or generated by the computer system  80 , such as production forecasts. Input devices  98  and output devices  100  may be provided as part of the computer system  80  or may be separately provided. 
     Although the preceding description has been described herein with reference to particular means, materials and embodiments, it is not intended to be limited to the particulars disclosed herein; rather, it extends to functionally equivalent structures, methods, and uses, such as are within the scope of the appended claims.