Well-based Surrogate Reservoir Model (SRM) may be classified as a new technology for building proxy models that represent large, complex numerical reservoir simulation models. The well-based SRM has several advantages over traditional proxy models, such as response surfaces or reduced models. These advantages include (1) to develop an SRM one does not need to approximate the existing simulation model, (2) the number of simulation runs required for the development of an SRM is at least an order of magnitude less than traditional proxy models, and (3) above and beyond representing the pressure and production profiles at each well individually, SRM can replicate, with high accuracy, the pressure and saturation changes at each grid block.Well-based SRM is based on the pattern recognition capabilities of artificial intelligence and data mining (AI&DM) that is also referred to as predictive analytics. During the development process the SRM is trained to learn the principles of fluid flow through porous media as applied to the complexities of the reservoir being modeled. The numerical reservoir simulation model is used for two purposes: (1) to teach the SRM the physics of fluid flow through porous media as applied to the specific reservoir that is being modeled, and (2) to teach the SRM the complexities of the heterogeneous reservoir represented by the geological model and its impact on the fluid production and pressure changes in the reservoir.Application of well-based SRM to two offshore fields in Saudi Arabia is demonstrated. The simulation model of these fields includes millions of grid blocks and tens of producing and injection wells. There are four producing layers in these assets that are contributing to production. In this paper we provide the details that is involved in development of the SRM and show the result of matching the production from the all the wells. We also present the validation of the SRM through matching the results of blind simulation runs.The steps in the development of the SRM includes design of the required simulation runs (usually less than 20 simulation runs are sufficient), identifying the key performance indicators that control the pressure and production in the model, identification of input parameters for the SRM, training and calibration of the SRM and finally validation of the SRM using blind simulation runs.
Application of the Surrogate Reservoir Model (SRM) to an onshore green field in Saudi Arabia is the subject of this paper. SRM is a recently introduced technology that is used to tap into the unrealized potential of the reservoir simulation models. High computational cost and long processing time of reservoir simulation models limit our ability to perform comprehensive sensitivity analysis, quantify uncertainties and risks associated with the geologic and operational parameters or to evaluate a large set of scenarios for development of green fields. SRM accurately replicates the results of a numerical simulation model with very low computational cost and low turnaround period and allows for extended study of reservoir behavior and potentials. SRM represents the application of artificial intelligence and data mining to reservoir simulation and modeling.In this paper, development and the results of the SRM for an onshore green field in Saudi Arabia is presented. A reservoir simulation model has been developed for this green field using Saudi Aramco's in-house POWERS™ simulator. The geological model that serves as the foundation of the simulation model is developed using an analogy that incorporates limited measured data augmented with information from similar fields producing from the same formations. The reservoir simulation model consists of 1.4 million active grid blocks, including 40 vertical production wells and 22 vertical water injection wells.Steps involved in developing the SRM are identifying the number of runs that are required for the development of the SRM, making the runs, extracting static and dynamic data from the simulation runs to develop the necessary spatio-temporal dataset, identifying the key performance indicators (KPIs) that rank the influence of different reservoir characteristics on the oil and gas production in the field, training and matching the results of the simulation model, and finally validating the performance of the SRM using a blind simulation run.SRM for this reservoir is then used to perform sensitivity analysis as well as quantification of uncertainties associated with the geological model. These analyses that require thousands of simulation runs were performed using the SRM in minutes.
Managers, geologists, reservoir and completion engineers are faced with important challenges and questions when it comes to producing from and operating shale assets. Some of the important questions that need to be answered are: What should be the distance between wells (well spacing)? How many clusters need to be included in each stage? What is the optimum stage length? At what point we need to stop adding stages in our wells (what is the point of diminishing returns)? At what rate and at what pressure do we need to pump the fluid and the proppant? What is the best proppant concentration? Should our completion strategy be modified when the quality of the shale (reservoir characteristics) and the producing hydrocarbon (dry gas, vs. condensate rich, vs. oil) changes in different parts of the field? What is the impact of soak time (starting production right after the completion versus delaying it) on production? Shale Analytics is the collection of the state of the art data driven techniques including artificial intelligence, machine learning, and data mining that addresses the above questions based on facts (field measurements) rather than human biases. Shale Analytics is the fusion of domain expertise (years of geology, reservoir, and production engineering knowledge) with data driven analytics. Shale Analytics is the application of Big Data Analytics, Pattern Recognition, Machine Learning and Artificial Intelligence to any and all Shale related issues. Lessons learned from the application of Shale Analytics to more than 3,000 wells in Marcellus, Utica, Niobrara, and Eagle Ford is presented in this paper along with a detail case study in Marcellus Shale. The case study details the application of Shale Analytics to understand the impact of different reservoir and completion parameters on production, and the quality of predictions made by artificial intelligence technologies regarding the production of blind wells. Furthermore, generating type curves, performing "Look-Back" analysis and identifying best completion practices are presented in this paper. Using Shale Analytics for re-frac candidate selection and design was presented in a previous paper [1].
A novel approach to reservoir management applied to a mature giant oilfield in the Middle East is presented. This is a prolific brown field producing from multiple horizons with production data going back to mid-1970s. Periphery water injection in this filed started in mid-1980s. The field includes more than 400 producers and injectors. The production wells are deviated (slanted) or horizontal and have been completed in multiple formations. An empirical, full field reservoir management technology, based on a data-driven reservoir model was used for this study. The model was conditioned to all available types of field data (measurements) such as production and injection history, well configurations, well-head pressure, completion details, well logs, core analysis, time-lapse saturation logs, and well tests. The well tests were used to estimates the static reservoir pressure as a function of space and time. Time-lapse saturation (Pulse-Neutron) logs were available for a large number of wells indicating the state of water saturation in multiple locations in the reservoir at different times. The data-driven, full field model was trained and history matched using machine learning technology based on data from all wells between 1975 and 2001. The history matched model was deployed in predictive mode to generate (forecast) production from 2002 to 2010 and the results was compared with historical production (Blind History Match). Finally future production from the field (2011 to 2014) was forecasted. The main challenge in this study was to simultaneously history match static reservoir pressure, water saturation and production rates (constraining well-head pressure) for all the wells in the field. History matches on a well-by-well basis and for the entire asset is presented. The quality of the matches clearly demonstrates the value that can be added to any given asset using pattern recognition technologies to build empirical reservoir management tools. This model was used to identify infill locations and water injection schedule in this field. RESERVOIR MANAGEMENT Reservoir management has been defined as use of financial, technological, and human resources, to minimizing capital investments and operating expenses and to maximize economic recovery of oil and gas from a reservoir. The purpose of reservoir management is to control operations in order to obtain the maximum possible economic recovery from a reservoir on the basis of facts, information, and knowledge (Thakur 1996). Historically, tools that have been successfully and effectively used in reservoir management integrate geology, petrophysics, geophysics and petroleum engineering throughout the life cycle of a hydrocarbon asset. Through the use of technologies such as remote sensors and simulation modeling, reservoir management can improve production rates and increase the total amount of oil and gas recovered from a field (Chevron 2012). Reservoir simulation and modeling has proven to be one of the most effective instruments that can integrate data and expertise fr...
Large CO 2 -enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO 2 -EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology.
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