Extraction of oil and gas can cause reduction in pore pressure, occasionally resulting in subsequent compaction that forms a surface subsidence bowl, especially in shallow reservoirs. In the last 10 years, there has been over 10 feet of subsidence in parts of the Lost Hills oil field in California (Bruno et al.,1992). The surface subsidence at Lost Hills not only causes damage to surface facilities and wells, but also reactivates faults and reduces rock permeability. Subsidence makes reservoir optimization difficult. Hence, it is important to assess or predict the surface subsidence and the reasons for subsidence early in the life of an oil field to make an optimization plan. We use jointly the capacitance resistance model (CRM) (Alberoni et al., 2002 and Yousef, et al., 2006) that relies only on injection and production data, and the InSAR satellite imagery of surface subsidence. From CRM simulations, we estimate the connectivity between injectors and producers as well as general water flow directions from individual injectors. We then superimpose well connectivity and InSAR imagery to diagnose the reasons for the subsidence. Using new surface subsidence models, which are based on the continuity equation of CRM and rock mechanics, we are able to predict the average surface subsidence at Lost Hills from the injection and production rates. Our work shows that there was significant volumetric rock damage at Lost Hills and the well connectivity changed dramatically with time because of reservoir compaction and the rock damage. We conclude that for a soft, fragile and nearly- impermeable rock such as the diatomite, high injection rate weakens the rock and creates dynamic water flow tubes or ‘channels’ without providing good pressure support to the reservoir. These high permeability ‘channels’ re-circulate most of the injected water between the injectors and producers. Our CRM/InSAR approach is new and gives insights into the time-dependent and spatially variable fluid flow fields in a relatively shallow waterflood. Consequently, we may be able to suggest optimum water injection strategies to enhance oil production, while minimizing rock damage and surface subsidence. In addition, the proposed surface subsidence models are convenient and reliable to predict the average surface subsidence. / text

The Capacitance Resistance Model (CRM) is a reservoir model based on a data-driven approach. It stems from the continuity equation and takes advantage of the usually abundant rate data to achieve a synergy of analytical model and data-driven approach. Minimal information (rates and bottom-hole pressure) is required to inexpensively characterize the reservoir. Important information, such as inter-well connectivity, reservoir compressibility effects, etc., can be easily and readily evaluated. The model also suggests optimal injection schemes in an effort to maximize ultimate oil recovery, and hence can assist real time reservoir analysis to make more informed management decisions. Nevertheless, an important limitation in the current CRM model is that it only treats the reservoir flow as single-phase flow, which does not favor capturing physics when the saturation change is large, such as for an immature water flood. To overcome this limitation, we develop a two-phase flow coupled CRM model that couples the pressure equation (fluid continuity equation) and the saturation equation (oil mass balance). Through this coupling, the model parameters such as the connectivity, the time constant, temporal oil saturation, etc., are estimated using nonlinear multivariate regression to history match historical production data. Incorporating the physics of two-phase displacement brings several advantages and benefits to the CRM model, such as the estimation of total mobility change, more accurate prediction of oil production, broader model application range, and better adaptability to complicated field scenarios. Also, the estimated saturation within the drainage volume of each producer can provide insights with respect to the field remaining oil saturation distribution. Synthetic field case studies are carried out to demonstrate the different capabilities of the coupled CRM model in homogeneous and heterogeneous reservoirs with different geological features. The physical meanings of model parameters are well explained and validated through case studies. The results validate the coupled CRM model and show improved accuracy in model parameters obtained through the history match. The prediction of oil production is also significantly improved compared to the current CRM model. A more reliable oil rate prediction enables further optimization to adjust injection strategies. The coupled CRM model has been shown to be fast and stable. Moreover, sensitivity analyses are conducted to study and understand the impact of the input information (e.g., relative permeability, viscosity) upon the output model parameters (e.g., connectivity, time constants). This analysis also proves that the model parameters from the two-phase coupled model can combine both reservoir compressibility and mobility effects. / text

Production data are the most abundant data in the field. However, they can often be of poor quality because of undocumented operational problems, or changes in operating conditions, or even recording mistakes (Nobakht et al. 2009). If this poor quality or inconsistency is not recognized as such, it can be misinterpreted as a reservoir issue other than the data quality problem that it is. Thus quality control of production data is a crucial and necessary step that must precede any further interpretation using the production data. To restore production data, we propose to use the capacitance resistance model (CRM) to realize data reconciliation. CRM is a simple reservoir simulation model that characterizes the connectivity between injectors and producers using only production and injection rate data. Because the CRM model is based on the continuity equation, it can be used to analyze the production corresponding to the injection signal in the reservoir. The problematic production data are then put into the CRM model directly and the resulting CRM output parameters are used to evaluate what the correct production response would be under current injection scheme. We also make sensitivity analysis based on synthetic fields, which are heterogeneous ideal reservoir models with imposed geology and well features in Eclipse. The aim is to show how bad data could be misleading and the best way to restore the production data. Using the CRM model itself to control data quality is a novel method to obtain clean production data. We can then apply the new clean production data in reservoir simulators or any other processes where production data quality matters. This data quality control process can help better understand the reservoir, analyze its behavior in a more ensured way and make more reliable decisions. / text

Much effort of reservoir engineers is devoted to the time-consuming process of history matching in a simulator to understand the reservoir complexity. Its accuracy is debatable because only a few inputs are known. Several analytical tools have been developed to investigate reservoir heterogeneity. The reciprocal productivity index (RPI) is a tool to measure the pressure support observed at a producer. The log (water-oil ratio or WOR) plot can be used to indicate the presence of a channel. A capacitance-resistance model (CRM) is a simple tool to estimate the connectivity between a producer-injector pair from the production/injection and pressure data. Generally field operators implement an improved recovery plan such as water-alternating-gas (WAG) flood to improve displacement efficiency. However, the existence of heterogeneity compromises its performance. The first objective of this study is to improve the assessment of tertiary flood performance by integrating the CRM with other analytical tools. The integrated method was applied to a miscible flood field in West Texas. The results suggest strong interwell connectivity found more frequently in the NE-SW direction and the different preferential flow paths of injected CO2 and water. Overall, the results provide insights into the current flood status. The operating conditions of a producer dynamically change because of well/field constraints. These changes can induce significant interference in other wells, which cannot be captured by CRM. The second objective of this study is to develop a capacitance-resistance model with producer-producer interaction (CRMP-P). The CRMP-P, derived from the continuity and Darcy’s equations, accounts for producer-producer interactions. The CRMP-P was applied to data from three different reservoir models. The results suggest that the CRMP-P could fit the data with higher precision than CRM. Consequently, the CRMP-P estimates of reservoir properties are more accurate. Moreover, the estimated transmissibility between producers is in agreement with the reservoir models. The CRMP-P was also applied to Omani field data. The transmissibility results are consistent with previous study and the drilling information. The more accurate information on producer-producer interactions and reservoir properties can assist in history-matching, locating infill wells, and reservoir management planning. / text

The capacitance-resistance model (CRM) has been continuously improved and tested on both synthetic and real fields. For a large waterflood, with hundreds of injectors and producers present in a reservoir, tens of thousands of model parameters (gains, time constants, and productivity indices) in a field must be determined to completely define the CRM. In this case obtaining a unique solution in history-matching large reservoirs by nonlinear regression is difficult. Moreover, this approach is more likely to produce parameters that are statistically insignificant. The nonlinear nature of the CRM also makes it difficult to quantify the uncertainty in model parameters. The analytical solutions of the two linear reservoir models, the linearly transformed CRM whose control volume is the drainage volume around each producer (ltCRMP) and integrated capacitance-resistance model (ICRM), are developed in this work. Both models are derived from the governing differential equation of the producer-based representation of CRM (CRMP) that represents an in-situ material balance over the effective pore volume of a producer. The proposed methods use a constrained linear multivariate regression (LMR) to provide information about preferential permeability trends and fractures in a reservoir. The two models’ capabilities are validated with simulated data in several synthetic case studies. The ltCRMP and ICRM have the following advantages over the nonlinear waterflood model (CRMP): (1) convex objective functions, (2) elimination of the use of solver when constraints are ignored, and (3) faster computation time in optimization. In both methods, a unique solution can always be obtained regardless of the number of parameters as long as the number of data points is greater than the number of unknowns (parameters). The methods of establishing the confidence limits on CRMP gains and ICRM parameters are demonstrated in this work. This research also presents a method that uses the ICRM to estimate the gains between newly introduced injectors and existing producers for a homogeneous reservoir without having to do additional simulations or regression on newly simulated data. This procedure can guide geoscientists to decide where to drill new injectors to increase future oil recovery and provide rapid solutions without having to run reservoir simulations for each scenario. / text