Ajay Pratap Singh

Causal Analysis and Data Mining of Well Stimulation Data Using Classification and Regression Tree with Enhancements

In the well-treatment program certain variables, like Job Pause Time (JPT) and fracture screen-out, can affect its efficiency. JPT is the time during which pumping is paused in-between subsequent treatments and screen-out occurs when the fluid flow is restricted inside the fracture. We investigate whether it is possible to identify characteristic patterns in existing data that affect the extreme values of JPT as well as the most critical variables causing fracture screen-out. We apply Classification and Regression Tree (CART) analysis, validate the approach with well-stimulation case studies and enhance predictive capability by implementing normal score transform and data clustering.

Next Generation of Workflows for Multilevel Assisted History Matching and Production Forecasting: Concept, Implementation and Visualization

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 describes the conceptualization, implementation, and visualization characteristics of the multilevel assisted history matching (AHM) technique 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. The inversion process is dramatically accelerated by the efficient parameterization of permeability, constraining the proxy model using streamline-based sensitivities and using parallel and cluster computing. 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. Introduction With the vision to transform the Kuwait Oil Company (KOC) through the application of integrated digital oilfield (iDOF) concepts and drive the future KOC operations to the next level of excellence, the operator’s senior management endorsed the development of a family of advanced integrated asset management (IAM) workflows, referred to as “smart flows,” to optimize and integrate the subsurface models of the major Sabriyah-Mauddud (SaMa) reservoir with well models and network surface systems in various time horizons. The objective is to increase the effectiveness through automating work processes and shortening observation-to-action cycle time. The group of nine first-generation production engineering workflows focuses on production and operational activities and was launched at KwIDF in 2012. The workflows are introduced in Al-Abbasi et al. (2013) and described in greater detail in Al-Jasmi et al. (2013) and references therein. The second generation of smart flows combines subsurface waterflooding optimization (SWFO) (Khan et al. 2013), integrated production optimization (IPO), and simulation model update and ranking (SMUR). The preceding publication, Maucec et al. (2013), briefly discusses the outstanding challenges of the model reconciliation and history matching and reviews the recent approaches the oil industry is taking to quantify the uncertainty and increase the accuracy of reservoir models. Moreover, in Maucec et al. (2013), the engineering concepts of SMUR smart flow are described in detail, combining the processes of building the high-resolution geocellular model and the associated reservoir simulation model, leading into a history-matching case study of the SaMa field. The design of the SMUR smart flow is leveraged with the technology for

Multivariate Analysis of Job Pause Time Data Using Classification and Regression Tree and Kernel Clustering

The well treatment program is an important part of the field development plan, and certain variables, such as job pause time (JPT), can affect its efficiency. JPT is the time during which pumping is paused between subsequent treatments of a job. 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. 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 using case studies first using classical CART analysis, then using CART analysis with enhancement tools such as the normal score transform (NST), and then dividing the large dataset into smaller groups using clustering. Significant variables are found that affect the response variables, and predictor variables are ranked in order of their importance. Such information can be used to control predictor variables that cause high JPT. The results are outlined in an intuitive way, including categorical, continuous, and missing values. Because CART is a data driven, deterministic model, we cannot calculate the confidence interval of the predicted response. Confidence in the results is purely based on the historical values, and the accuracy of the result produced by a tree model depends on the quality of the recorded data measured in terms of volume, reliability, and consistency. The prediction capability of CART is enhanced by the use of NST and clustering techniques. The approach presented in this paper analyzes a dataset with limited information and high uncertainty and should lead to developing a method for generating proxy models to find future success indices (e.g., for drilling efficiency or production from a fracture). This could standardize stimulation and generate decision ‘best practices’ to save costs in field development and the optimization process.

A Method for Multi-Level Probabilistic History Matching and Production Forecasting: Application in a Major Middle East Carbonate Reservoir

We present a probabilistic, computer-assisted history-matching method that captures inherent model uncertainty, enhances the predictive value of reconciled models, and renders more accurate production forecasts that help reservoir characterization and ultimately improve oil recovery factor. The workflow interfaces between the geo-modeling application and reservoir simulator, preserves the geological detail by updating high-resolution models and identifies models with highest potential in oil-recovery. We successfully apply this automated workflow to history matching 50 years of oil production, 12 years of water injection and 8 years of forecasting in the pilot area in major, structurally complex Middle East carbonate reservoir.