Calibrate Flow Simulation Models with Well-Test Data to Improve History Matching

Author(s):  
Nanqun He ◽  
K.T. Chambers
2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.


2015 ◽  
Vol 18 (04) ◽  
pp. 481-494 ◽  
Author(s):  
Siavash Nejadi ◽  
Juliana Y. Leung ◽  
Japan J. Trivedi ◽  
Claudio Virues

Summary Advancements in horizontal-well drilling and multistage hydraulic fracturing have enabled economically viable gas production from tight formations. Reservoir-simulation models play an important role in the production forecasting and field-development planning. To enhance their predictive capabilities and to capture the uncertainties in model parameters, one should calibrate stochastic reservoir models to both geologic and flow observations. In this paper, a novel approach to characterization and history matching of hydrocarbon production from a hydraulic-fractured shale is presented. This new methodology includes generating multiple discrete-fracture-network (DFN) models, upscaling the models for numerical multiphase-flow simulation, and updating the DFN-model parameters with dynamic-flow responses. First, measurements from hydraulic-fracture treatment, petrophysical interpretation, and in-situ stress data are used to estimate the initial probability distribution of hydraulic-fracture and induced-microfracture parameters, and multiple initial DFN models are generated. Next, the DFN models are upscaled into an equivalent continuum dual-porosity model with analytical techniques. The upscaled models are subjected to the flow simulation, and their production performances are compared with the actual responses. Finally, an assisted-history-matching algorithm is implemented to assess the uncertainties of the DFN-model parameters. Hydraulic-fracture parameters including half-length and transmissivity are updated, and the length, transmissivity, intensity, and spatial distribution of the induced fractures are also estimated. The proposed methodology is applied to facilitate characterization of fracture parameters of a multifractured shale-gas well in the Horn River basin. Fracture parameters and stimulated reservoir volume (SRV) derived from the updated DFN models are in agreement with estimates from microseismic interpretation and rate-transient analysis. The key advantage of this integrated assisted-history-matching approach is that uncertainties in fracture parameters are represented by the multiple equally probable DFN models and their upscaled flow-simulation models, which honor the hard data and match the dynamic production history. This work highlights the significance of uncertainties in SRV and hydraulic-fracture parameters. It also provides insight into the value of microseismic data when integrated into a rigorous production-history-matching work flow.


2008 ◽  
Vol 2008 ◽  
pp. 1-13 ◽  
Author(s):  
Tina Yu ◽  
Dave Wilkinson ◽  
Alexandre Castellini

Reservoir modeling is a critical step in the planning and development of oil fields. Before a reservoir model can be accepted for forecasting future production, the model has to be updated with historical production data. This process is called history matching. History matching requires computer flow simulation, which is very time-consuming. As a result, only a small number of simulation runs are conducted and the history-matching results are normally unsatisfactory. This is particularly evident when the reservoir has a long production history and the quality of production data is poor. The inadequacy of the history-matching results frequently leads to high uncertainty of production forecasting. To enhance the quality of the history-matching results and improve the confidence of production forecasts, we introduce a methodology using genetic programming (GP) to construct proxies for reservoir simulators. Acting as surrogates for the computer simulators, the “cheap” GP proxies can evaluate a large number (millions) of reservoir models within a very short time frame. With such a large sampling size, the reservoir history-matching results are more informative and the production forecasts are more reliable than those based on a small number of simulation models. We have developed a workflow which incorporates the two GP proxies into the history matching and production forecast process. Additionally, we conducted a case study to demonstrate the effectiveness of this approach. The study has revealed useful reservoir information and delivered more reliable production forecasts. All of these were accomplished without introducing new computer simulation runs.


1998 ◽  
Vol 1 (03) ◽  
pp. 201-206 ◽  
Author(s):  
M.J. Mavor ◽  
J.E. Vaughn

Summary Recently measured data show that the absolute permeability of coal natural fracture is increasing significantly with continued gas production in the San Juan basin Fruitland formation. This phenomenon caused gas-production rates to be many times greater than expected from early production history. The phenomenon also caused producing bottomhole pressures to increase when gas rates were constant, opposite from that expected from conventional applications of Darcy's law. The increase in absolute permeability caused by gas desorption has been measured on cores, but, until recently, there was no verification that this phenomenon occurs in situ. Palmer and Mansoori (P&M) presented a new theory and showed how this theory matched gas- and water-production rates and estimated bottomhole-pressure data for a high-deliverability San Juan basin Fruitland formation coal-gas well. However, Palmer and Mansoori had no transient pressure data to support in-situ permeability changes. This paper documents data from drill stem tests (DST's) and shut-in tests with analyses there of and additional production-rate and pressure behaviors that support the P&M theory. The well-test data were measured in three wells completed in the San Juan basin Fruitland formation coal seams located under the Valencia Canyon (VC) area. These wells, VC 29-4, VC 32-1, and VC 32-4, are located in Sections 29 and 32, T33N, RllW, La Plata County, Colorado, and operated by EnerVest San Juan Operating LLC. In addition to the well-test data, EnerVest and the Gas Research Inst. (GRI) collected extensive formation-evaluation data and performed detailed analyses that allowed a thorough description of the area. Although there are other wells operated by EnerVest in the area, well-test data were not available from the other wells to determine the absolute permeability estimates; therefore, these wells are not discussed in this paper. The P&M theory was calibrated with well-test-derived absolute permeability estimates and published coal-shrinkage data. Reservoir simulation models, based on the calibrated theory, matched the unusual producing, bottomhole-pressure behavior. Without the P&M theory it was not possible to match pressure behavior or permeability estimates with reasonable variations of reservoir properties input into the reservoir-simulation models. The remainder of this paper summarizes the well-test analysis results from the three wells and, for brevity, one set of well-test data and one simulation study. The well-test data and simulation studies for the other wells were similar to the examples.


2018 ◽  
Vol 488 (1) ◽  
pp. 237-257 ◽  
Author(s):  
Patrick William Michael Corbett ◽  
Gleyden Lucila Benítez Duarte

AbstractTwo decades of geological modelling have resulted in the ability to study single-well geological models at a sufficiently high resolution to generate synthetic well test responses from numerical simulations in realistic geological models covering a range of fluvial styles. These 3D subsurface models are useful in aiding our understanding and mapping of the geological variation (as quantified by porosity and permeability contrasts) in the near-wellbore region. The building and analysis of these models enables many workflow steps, from matching well test data to improving history-matching. Well testing also has a key potential role in reservoir characterization for an improved understanding of the near-wellbore subsurface architecture in fluvial systems. Developing an understanding of well test responses from simple through increasingly more complex geological scenarios leads to a realistic, real-life challenge: a well test in a small fluvial reservoir. The geological well testing approach explained here, through a recent fluvial case study in South America, is considered to be useful in improving our understanding of reservoir performance. This approach should lead to more geologically and petrophysically consistent models, and to geologically assisted models that are both more correct and quicker to match to history, and thus, ultimately, to more useful reservoir models. It also allows the testing of a more complex geological model through the well test response.


SPE Journal ◽  
1996 ◽  
Vol 1 (02) ◽  
pp. 145-154 ◽  
Author(s):  
Dean S. Oliver

2008 ◽  
Vol 11 (06) ◽  
pp. 1071-1081 ◽  
Author(s):  
Amy Whitaker ◽  
C. Shah Kabir ◽  
Wayne Narr

Summary The extent to which fractures affect fluid pathways is a vital component of understanding and modeling fluid flow in any reservoir. We examined the Wafra Ratawi grainstone for which production extending for 50 years, including recent horizontal drilling, has provided some clues about fractures, but their exact locations, intensity, and overall effect have been elusive. In this study, we find that a limited number of total fractures affect production characteristics of the Ratawi reservoir. Although fractures occur throughout the Wafra field, fracture-influenced reservoir behavior is confined to the periphery of the field where the matrix permeability is low. This work suggests that for the largest part of the field, explicit fractures are not necessary in the next-generation Earth and flow-simulation models. The geologic fracture assessment included seismic fault mapping and fracture interpretation of image logs and cores. Fracture trends are in the northeast and southwest quadrants, and fractures are mineralized toward the south and west of the field. Pressure-falloff tests on some peripheral injectors indicate partial barriers, and most of these wells lie on seismic-scale faults in the reservoir, suggesting partial sealing. A few wells show fractured-reservoir production characteristics, and rate-transient analysis on a few producers indicates localized dual-porosity behavior. Producers proximal to dual-porosity wells display single-porosity behavior, however, to attest to the notion of localized fracture response. The spatially restricted fracture-flow characteristics appear to correlate with fracture or vug zones in a low-permeability reservoir. Presence of fracture-flow behavior was tested by constructing the so-called flow-capacity index (FCI), the ratio of khwell (well test-derived value) to khmatrix (core-derived property). Data from 80 wells showed khmatrix to be consistently higher than khwell, a relationship that suggests insignificant fracture production in these wells. Introduction The Wafra field is in the Partitioned Neutral Zone (PNZ) between Kuwait and Saudi Arabia, as shown in Fig. 1. The field has been producing since the 1950s and has seen renewed drilling activity since the late 1990s, including horizontal drilling and implementation of peripheral water injection (Davis and Habib 1999). The Lower Cretaceous Ratawi formation contains the most reserves of the producing intervals at Wafra. The Ratawi oolite (a misnomer--it is a grainstone) reservoir has variable porosity (5 to 35%) and permeability that ranges from tens to hundreds of md (Longacre and Ginger 1988). The main Wafra structure is a gentle (i.e., interlimb angle >170°), doubly plunging anticline trending north-northwest to south-southeast, which culminates near its northern end. The East Wafra spur is a north-trending branch that extends from the center of the main Wafra structure. As seen in Fig. 1, relief on the Main Wafra structure exceeds that on East Wafra. The Ratawi oolite in the Wafra field has been studied at length, and various authors have reported geologic and engineering elements, leading to reservoir characterization and understanding of reservoir performance. Geologic studies are those of Waite et al. (2000) and Sibley et al. (1997). In contrast, Davis and Habib (1999) presented implementation of peripheral water injection, whereas Chawathé et al. (2006) discussed realignment of injection pattern owing to lack of pressure support in the reservoir interior. Previous studies considered the reservoir to behave like a single-porosity system. But recent image-log fracture interpretations indicate high fracture densities, suggesting that the implementation of a dual-porosity model may be necessary because the high impact of fractures during field development has been recognized in some Middle East reservoirs for more than 50 years (Daniel 1954). Static and dynamic data are required to characterize fracture reservoir behavior accurately (Narr et al. 2006). Geologic description of the fracture system, by use of cores, borehole images, seismic data, and well logs, does not in itself determine whether fractures affect reservoir behavior. While seismic and some image logs were available to locate fractures in the Wafra Ratawi reservoir, no dynamic testing with the specific objective of understanding fracture impact has occurred. So, to determine whether fractures influence oil productivity significantly, we used diagnostic analyses of production data and well tests of available injectors. The assessment of fracture effects in the Ratawi reservoir will be used to guide the next generation of geologic and flow-simulation models. Dynamic data involving pressure and rate have the potential to reveal the influence of open fractures in production performance. Unfortunately, pressure-transient testing on single wells does not always provide conclusive evidence about the presence of fractures with the characteristic dual-porosity dip on the pressure-derivative signature (Bourdet et al. 1989). That is because a correct mixture of matrix/fracture storativity must be present for the characteristic signature to appear (Serra et al. 1983). In practice, interference testing (Beliveau 1989) between wells appears to provide more-definitive clues about interwell connectivity, leading to inference about fractures. In contrast to pressure-transient testing, rate-transient analysis offers the potential to provide the same information without dedicated testing. In this field, all wells are currently on submersible pumps. Consequently, the pump-intake pressure and measured rate provided the necessary data for pressure/rate convolution or rate-transient analysis. We provide the Ratawi-reservoir case study primarily as an example of the integration of diverse geologic and engineering data to develop an assessment of fracture influence on reservoir behavior. It illustrates the use of production-data diagnostic tests to determine fracture influence in the absence of targeted fracture-analysis testing. The workflow can be applied to similar static/dynamic problems, such as fault-transmissivity determination. Secondly, this analysis illustrates the process of deciding that fractures, although present throughout the reservoir, may not lead to widespread fractured-reservoir characteristics (e.g., Allan and Sun 2003).


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