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Results
Abstract Enhanced Oil Recovery is firmly established in the hydrocarbon maturation plans of PDO and Shell with successful ongoing thermal and polymer field developments and many novel methods being piloted. The current developments and trials undergo in-depth study to leverage the learnings for portfolio-wide application. In optimizing a current or derisking a future EOR development, understanding of the reservoir is key and more crucial than during primary and secondary development because of the complexity of the processes and higher costs involved. In this paper the use of an advanced history matching technique for improving reservoir understanding is discussed in the context of full field chemical flooding. A powerful feature of the Shell proprietary reservoir simulator is the availability of the so-called adjoint method which allows history matching of gridblock parameters such as permeability and porosity in an efficient and effective manner. The method is applied in a workflow coined model maturation (Joosten & Altintas (2011)), where the local permeability updates are used to highlight deficiencies in the geological model (e.g. missing faults, aquifer locations, pinch outs). In this particular study the adjoint method improved the pattern match significantly but more importantly revealed the need for fine scale local geological features, confirmed by other data. The improved models describe the observed polymer behavior in line with our theoretical and analytical understanding of polymer flooding. The study showed that understanding EOR processes can only be done after a proper understanding of the reservoir.
- Asia > Middle East (0.30)
- North America > United States (0.29)
- Europe > United Kingdom (0.28)
- Europe > United Kingdom > North Sea > Southern North Sea > Southern Gas Basin > Silver Pit Basin > Block 49/30c > Davy Fields > Brown Field > Rotliegend Formation (0.99)
- Asia > Russia > Ural Federal District > Khanty-Mansi Autonomous Okrug > West Siberian Basin > Central Basin > Salymskoye Field (0.99)
Next Generation of Workflows for Multilevel Assisted History Matching and Production Forecasting: Concept, Implementation and Visualization
Maucec, M.. (Halliburton) | Singh, A. P. (Halliburton) | Carvajal, G. A. (Halliburton) | Mirzadeh, S.. (Halliburton) | Knabe, S. P. (Halliburton) | Mahajan, A.. (Halliburton) | Dhar, J.. (Halliburton) | Al-Jasmi, A. K. (Kuwait Oil Company) | El Din, I. H. (Kuwait Oil Company)
Abstract 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.
- Asia > Middle East > Kuwait (0.69)
- North America > United States > Texas (0.46)
New Approach to Validate History Matching Process
Abdel-Rahman, M. R. (Schlumberger Information Solution Middle East) | Dharmawan, M. A. (Schlumberger Information Solution Middle East) | Abdelrahim, R.. (Schlumberger Information Solution Middle East) | Akhtar, M. N. (Schlumberger Information Solution Middle East) | Mekki, N.. (Schlumberger Information Solution Middle East) | Al-Namlah, S. A. (Schlumberger Information Solution Middle East)
Abstract Conducting a history matching process is the most challenging and time consuming phase for reservoir simulation domain. It is worthy to invest more efforts to introduce new approaches to validate history matching results. This to guaranty the 3D model calibration has been adapted to adequate level that will minimize associated risk for the proposed forecast that completely rely on the history matching quality. This paper is describing a new approach for validating history matching results. It describes new workflow that is used to integrate all historical information comes from field surveillance and shows change of fluid distribution over field production history, e.g. observation of oil water contact encroachment, water coning, etc. Petrel™ as a 3D modeling package is used to integrate all of these information in automated way to build conceptual 3D fluid distribution models at selected time increments that represent the field production history. The workflow has two main steps, 1 to use geo-statistical approach to interpolate available fluid change surveillances at well level to build fluid contact surfaces at selected time increments. 2 step, Petrel™ will use each contact surface individually to build corresponding 3D conceptual fluid distribution model to describe fluid profile in 3D at a time. These conceptual models will simulate the overall mechanism of subsurface fluid movements over the production history based on the available historical data. By the end, the generated conceptual 3D fluid distribution models can be used to validate the fluid saturation models come from any finite difference simulator. It will do help to have better understanding of field performance in response to fluid contact change. In additional; It can guide a preliminary plan to propose infill wells for field development plans. This paper shows a detailed workflow of a mechanistic study to cover this new approach of validating the History Matching process and shed a light on the challenges and limitations of this new approach.