Traditional reconciliation of geomodels with production data is one of the most laborious tasks in reservoir engineering. The uncertainty associated with the great majority of model variables only adds to the overall complexity. This paper introduces an engineering workflow for probabilistic assisted history matching that captures inherent model uncertainty and allows for better quantification of production forecasts. The workflow is applied to history matching of the pilot area in a major, structurally complex Middle East (ME) carbonate reservoir. The simulation model combines 49 wells in five waterflood patterns to match 50 years of oil production and 12 years of water injection and to predict eight years of production. Initially, the reservoir model was calibrated to match oil production by modifying permeability and/or porosity at well locations and by fine-tuning rock-type properties and water saturation. The second level history match implemented two-stage Markov chain Monte Carlo (McMC) stochastic optimization to minimize the misfit in water cut on a well-by-well basis. While relative to evolutionary algorithms or the ensemble Kalman filter (EnKF), the McMC methods provide a statistically rigorous alternative for sampling posterior distribution; when deployed in direct simulation, they impose a high computational cost. The approach presented here accelerates the process by parameterizing the permeability using discrete cosine transform (DCT), constraining the proxy model using streamline-based sensitivities and utilizing parallel and cluster computing. While probabilistic assisted history matching (AHM) successfully reduced the misfit for most producing wells, the computational convergence was sensitive to the level of preserved geological detail. The optimal number of representative history-matched models was identified to capture the uncertainty in reservoir spatial connectivity using rigorous optimization and dynamic model ranking based on forecasted oil recovery factors (ORFs). The reduced set of models minimized the computational load for forecast-based analysis, while retaining the knowledge of the uncertainty in the recovery factor. The comprehensive probabilistic AHM workflow was implemented at the operator's North Kuwait Integrated Digital Oilfield (KwIDF) collaboration center. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with geological, production, and completion information. This allows engineers to improve the reservoir characterization and identify the areas that require more data capture.
The well-treatment program is an important part of the fielddevelopment plan, and certain variables, such as job-pause time (JPT) and fracture screenout, can affect its efficiency. JPT is the time during which pumping is paused between subsequent treatments of a job. Screenout occurs because of a sudden restriction of fluid flow inside the fracture and through the perforation. The objectives of this work are to investigate whether, from existing data, it is possible to find patterns in significant variables that affect the extreme values of JPT in a particular region, and what is the most critical variable causing screenout. The answers are sought by applying a classification-and-regression tree (CART) to both categorical and continuous variables in the database. The practical application of CART is presented by use of case studies containing JPT and screenout. Significant variables are found that affect the response variables, and predictor variables are ranked in the hierarchal order of their importance. Such information can be used to control predictor variables that cause high JPT or screenout.The results are outlined in an intuitive way, including categorical, continuous, and missing values. Because CART is a datadriven, deterministic model, one cannot calculate the confidence interval of the predicted response. The confidence in results is purely because of historical values, and the accuracy of the result produced by a tree model depends on the quality of recorded data measured in terms of volume, reliability, and consistency. The prediction capability of CART is enhanced by use of the normalscore transform and by dividing the data set into smaller groups by use of clustering. The approach presented in this paper analyzes a data set under limited information and high uncertainty and should lead to developing methodology for generating proxy models to find future success indices (e.g., one for drilling efficiency or production from a fracture). This could standardize stimulation and generate decision practices to save costs in field development and the optimization process.
History matching processes for complex and large reservoirs have always posed difficulties to reservoir engineers. To help reservoir engineers during history matching, various assisted history matching (AHM) algorithms have been developed. While AHM can help automate various aspects of history matching, oftentimes, algorithms suffer from slow convergence. This work proposes an ensemble based markov-chain Monte Carlo (MCMC) based algorithm with efficient sampling from the given distribution of properties. For efficient sampling properties during AHM, streamline trajectories are used to find the connection between source(s) and producer well. Streamline tracking based on output of the full-physics simulator is used as a guideline to capture the fluid flow patterns, and only properties of grid cells along the streamline trajectories are considered prime candidates for history matching. The proposed algorithm was applied to a sector model of a reservoir as a test case study to history match water cut on a well-by-well basis.
The paper presents an application of the regression tree technique as a root-cause identification and production diagnostic tool, and presents case studies for gas wells using the plunger-lift system. The regression tree model is data-driven and easy to construct, and does not require all of the parameters that a first principle-based model often requires. Thus, these models are quite useful in cases where real-time data or the required data for a detailed analysis are unavailable. To improve prediction capability, a cross-validation technique was used to optimize tree size. A total of 8–10 variables that could potentially impact the gas production rate were considered, with case studies conducted on actual field data. Questions, such as which group of wells are performing well or poorly, are quickly answered. Regression tree analysis, when applied at the field level, can identify a group of wells that underperform compared to other wells. As a result, an asset team can prioritize wells identified as poor performers. Further, a statistical analysis helps to gain insight into understanding the causal relationship between the gas production rate and various operational variables. Based on this analysis, recommendations are also provided to improve the gas production rate of poorly performing wells. Individual well models were also constructed to identify the root causes for high or low gas production based on operational changes made over a certain period of time. Only a few of the variables were found to have a significant impact on gas production. The interpretation of the decision tree indicated that additional operational variables, such as production time and pressure build-up time, should be included in the regression tree analysis to improve the diagnostic process. This paper summarizes these conclusions and recommends future work to improve the prediction capability of the regression tree analysis.
This paper outlines the visualization and collaboration attributes of an automated workflow that integrates the computerassisted history matching (AHM), quantification of inherent model uncertainty, and optimization on production-forecast decisions. The workflow belongs to the group of smart flows for integrated asset management installed at the North Kuwait Integrated Digital Field (KwIDF) collaboration center.The workflow is facilitated through four interactive user interfaces: Dashboard: displays history-match indices for water cut and visualizes maps of permeability, porosity, water and oil saturation, reservoir quality index, and reservoir pressure. Field and Well History Matching: displays well-level history matching and forecasting results filtered by water cut, bottomhole pressure (BHP), and liquid rate and visualizes the distributions of corresponding errors per simulated scenario. Dynamic Ranking: categorizes and ranks trends of forecasted oil recovery for history-matched models using multidimensional scaling and clustering techniques and visualizes identified P10, P50, and P90 models. Property Comparison: displays permeability maps for prior and history-matched models to identify the regions of improvement in terms of reservoir heterogeneity. Additionally, streamline trajectories colored by the time-of-flight provide excellent visualization of reservoir connectivity.The workflow was applied in the pilot area of a major Middle East carbonate reservoir in North Kuwait and performs complex history matching and production forecasting. The simulation model combines 49 wells in 5 waterflood patterns to match 50 years of oil production, 12 years of water injection, and 8 years of forecasting.The differentiator of this workflow is that it is unique in direct interfacing between the geomodeling application and reservoir simulator and in updating of high-resolution models with no upscaling. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with production, completion, and geological information. This allows engineers to improve the reservoir characterization and identify the areas that require more data capture.
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