Ahmad Al-Jasmi

Oil Production Optimization in an Integrated Digital Field: The KOC Burgan Pilot Project Experience

Kuwait Oil Company (KOC) launched the Kuwait Integrated Digital Field GC1 (KwIDF-GC1) pilot project in 2009 as an investigation into how a cross-functional and cross-domain infrastructure could be established to aid in the achievement of corporate goals set for the following two decades. The company’s vision for 2030 includes a philosophical shift in the way that the country’s workforce accomplishes its tasks, employing the latest technologies and work processes. The project solution integrates field instrumentation, workflows automated in software, and focused collaboration. The Burgan oil field, the second largest in the world and the largest clastic reservoir, was discovered in 1938 and commercial oil production from it began in 1946. Production peaked in 1972 at around 2,400,000 barrels per day, and declined to around 1,700,000 barrels per day by 2005 [Croft 2013, Cordahi and Critchlow 2005]. Management of the reservoir has become increasingly challenging, partly due to damage that was incurred as the Iraqi invaders set fire to the wells during their retreat in 1991. This project is a first in the State of Kuwait to instrument oil wells with pressure and temperature gauges, multiphase water cut meters and remotely automated chokes. Automation of the field was the first step in providing the advanced technology required of this project, realizing tangible advantages in minimizing the health, safety and environmental (HSE) exposure of field personnel. Wellsite data can be read, and choke positions can be set, remotely at the gathering center without the need for field personnel to enter hazardous areas. Work processes were converted into automated digital workflows supported by advanced network modeling and nodal analysis software in a state-of-the-art collaboration center. The collaborative teams use optimization and visualization software to contribute in real time to production operations that optimize production gains. Integration of multidisciplinary teams such as field development, sub-surface, production operations and maintenance in a real-time work environment enables proactive and reliable decisions to be made much more quickly than in traditional environments with disparate work teams. This paper describes how the various technologies and work processes are used by the collaboration teams during the pilot project to increase efficiencies in oil production.

Engineering Workflow for Probabilistic Assisted History Matching and Production Forecating: Application to a Middle East Carbonate Reservoir

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. Introduction As part of a comprehensive strategy to transform the Kuwait Oil Company (KOC) through the application of digital oilfield (DOF) concepts, KOC initiated an assessment of the major Sabriyah-Mauddud (SaMa) reservoir for conversion to an integrated digital oilfield (iDOF) master platform, with the goal of increasing 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. With the vision to drive future KOC operations to the next level of excellence and to realize a large return on the investment in iDOF, the operator’s senior management endorsed the development of a family of advanced integrated asset management (IAM) workflows, referred to as “smartflows,” to optimize and integrate the subsurface models with well models and network surface systems in various time horizons.

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

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.