Reservoir Simulation and Uncertainty Analysis of Enhanced CBM Production Using Artificial Neural Networks

Coalbed methane is becoming one of the major natural gas resources. CO2 injection into CBM reservoirs is used as an effective method for CBM production enhancement (ECBM) and for long term sequestration of CO2 (CO2Seq). Reservoir simulation is used regularly for building representative ECBM and CO2Seq models. Given the wide range of uncertainties that are associated with the geological models (that forms the foundation of any reservoir simulation), comprehensive analysis and uncertainty quantification of ECBM and CO2Seq models become very time consuming if not impossible. This paper addresses the uncertainty quantification of a complex ECBM reservoir model. We use a new technique by developing a Surrogate Reservoir Model (SRM) that can accurately mimic the behavior of the commercial reservoir model. Upon validation of SRM, we perform Monte Carlo Simulation (MCS) in order to quantify the uncertainties associated with the geological (CBM) model. Performing MCS requires thousands of simulation runs that can be performed easily once the SRM is developed. Key Performance Indicators (KPI) of the simulation model are identified to help reservoir engineers concentrate on the most influential parameters on the model’s output when studying the reservoir and performing uncertainty analysis. Unlike conventional geo-statistical techniques that require hundreds of runs to build a response surface or a proxy model, building an SRM only requires a few simulation runs. Introduction Reservoir simulation provides information on the behavior of the reservoir under various production and/or injection scenarios. Reservoir engineers and managers use reservoir simulators to better understand the reservoir, perform future performance predictions and uncertainty analysis. Because of non-uniqueness of simulation models and uncertainties associated with the geo-cellular model (reservoir parameters), uncertainty analysis becomes an important task that is required for making operational decisions, since such decision making process necessitates the quantification of model uncertainties. Different techniques are used to quantify the uncertainties associated with reservoir parameters. MCS is a technique that is widely used in the oil and gas industry for the purpose of uncertainty analysis. Since MCS uses a statistical representation of parameters being studied, it requires thousands of reservoir realizations in order to provide a meaningful (statistically representative) conclusion on the effect of uncertain parameters on the model’s performance. Generating thousands of simulation models especially in case of large and complex models, which could take a long time to make a single simulation run, is impractical. Attempts have been made to perform uncertainty analysis with as small number of realizations as possible. Common techniques that have gained popularity in the oil and gas industry are the Experimental Design technique and Reduced Models. Response Surface Models are generated in order to analyze the results obtained from Experimental Design. Experimental Design has been used in reservoir simulation since 1990s. It is used to get maximum information at the lowest experimental cost, by changing all the uncertain parameters simultaneously. It is essentially an equation derived from all the multiple regressions of all the main parameters that affect the reservoir’s response (1) . Many studies have shown that by using the Experimental Design the reservoir model still needs to be run hundreds of times. Reduced Models are approximations of full three dimensional numerical simulation models that approach an analytical model for tractability (2) . This paper presents the application of a recently developed technique for reservoir simulation and modeling, called Surrogate Reservoir Modeling (SRM), to model and analyze an enhanced coalbed methane project. The CBM reservoir used in this analysis is a synthetic reservoir with characteristics representative of a coal in the Appalachian Basin. All the reservoir 2 Modeling & Uncertainty Analysis of ECBM Using ANN SPE 125959 simulation is performed using a commercial reservoir simulator (3) . Methodology Surrogate Reservoir Models are essentially Artificial Neural Networks that behave like a reservoir simulation model. The key to successful SRM development is design, preparation and compilation of reservoir simulation runs and results in a manner that is most appropriate for use with Artificial Intelligence and Data Mining (AI&DM) techniques such as neural networks and fuzzy systems. Once trained, the SRM can run thousands of simulation runs in a matter of seconds. Also, the number of reservoir realizations required to develop the SRM is significantly small when compared to other techniques. The reason SRMs can be developed with a small number of realizations is due to the way a single reservoir model is presented to the SRM. Interested readers are encouraged to review other published papers by the authors to learn more about SRMs (4)(5)(6)(7) . In this study, an Enhanced Coalbed Methane (ECBM) reservoir is analyzed. An Artificial Neural Network (ANN) is trained as the Surrogate Reservoir Model (SRM). The developed SRM can be considered a prototype of the full-field reservoir model that was developed earlier using a commercial reservoir simulator. Model Information The synthetic reservoir used in this study is a single-layer coal with 13 Pinnate pattern wells (wells with branching laterals also known as fishbone). Production from the reservoir starts at the beginning of year 2000 (start of the simulation) from all the wells producing at a constant Bottom-Hole Pressure (BHP) of 50 psia. Primary production continues for 2 years. Figure 1 is the structure of the CBM reservoir modeled in this study. Figure 1: Structure of the CBM reservoir. Grid tops are shown in this figure. After the completion of primary production from all thirteen wells, four wells at the bottom-left corner of the reservoir (indicated as Group 1 in Figure 1) are converted into injectors. At the same time, as these four wells are converted into injectors, the next four wells (indicated as Group 2 in Figure 1) are shut in for the rest of the simulation time, and the remaining five wells (indicated as Group 3 in Figure 1) continue producing for the rest of the simulation time (the end of 2015). The objective of this study was to develop an SRM that can predict CH4 and CO2 production of group 3 wells as a function of CO2 injection rate of group 1 wells. Data from the first 5 years of production is introduced to the network and the network will predict the wells’ production for the next 10 years. Also, using the developed SRM, uncertainty analysis is performed on the reservoir parameters that were used in the model. As part of the SRM development process, an elemental volume is defined in the reservoir that is a function of the SPE 125959 Jalali and Mohaghegh 3 number of the wells. An Estimated Ultimate Drainage Area (EUDA) is identified for each well using Voronoi graph theory (8) . Then the EUDA is divided into four segments making a total of 52 segments for the entire reservoir. Static and dynamic properties then are averaged for these segments. The segment properties are introduced to the SRM in order to provide a picture of the reservoir’s characteristics. SRM dataset is divided into cell-based and well-based data. Cell-based data are the reservoir properties, such as depth, thickness, porosity, permeability, etc. Well-based data include well location, well configuration information, and well production data. Tables 1 and 2 are the list of cell-based and well-based data used in this study, respectively. Note that reference points mentioned in these tables refer to specific times that the reservoir properties are calculated. Reference points 1, 2, and 3 are years 2000, 2002, and 2005, respectively. Table 1: Cell-based data used for SRM development. Cell-Based Data used as input data to SRM CH4 adsorption @ reference points 2 and 3 CO2 adsorption @ reference point 3 Fracture CH4 mole fraction @ reference point 3 Fracture CO2 mole fraction @ reference point 3 Matrix CH4 mole fraction @ reference point 3 Matrix CO2 mole fraction @ reference point 3 Fracture Gas saturation @ reference points 2 and 3 Fracture pressure @ reference points 2 and 3 Water saturation @ reference points 2 and 3 Permeability porosity Thickness Table 2: Well-based data used for SRM development. Well-Based Data used as input data to SRM Cumulative CH4 production of 3 offset wells from 2000 to 2005 Cumulative CO2 production of 3 offset wells from 2000 to 2005 Well location X Well location Y Well’s main leg length Well’s first lateral length Well’s second lateral length Well’s third lateral length Well’s total length CO2 injection rate of 4 injectors @ 2002 and 2005 Date Distance from 3 offset wells Cumulative CH4 Production of the 3 offset wells from 2000 to 2005 Cumulative CO2 Production of the 3 offset wells from 2000 to 2005 During the SRM development, input parameters are ranked based on their influence on the model’s output. This process is important especially when the number of input parameters is high and the engineer has to choose a limited number of parameters as input for the SRM. The parameters that have the highest impact on the model’s output are called Key Performance Indicators (KPIs). Figure 2 shows the schematic of the well pattern used for all the wells in the reservoir and SRM segments. Cellbased properties are averaged for these segments and introduced to the SRM as input data. Figure 2: Shows an schematic of well branches and SRM segments. We assume to know the reservoir’s production for the first 5 years from 2000 to 2005. This usually is the case when a history matched model is going to be used for field development strategies. We are assuming that the model has been history matched with field production from 2000 to 2005. Therefore, some of