Summary This paper provides a summary and a guide of the enhanced-oil-recovery (EOR) technologies initiated in the North Sea in the period from 1975 until beginning of 2005. The five EOR technologies that have been initiated in this region are hydrocarbon (HC) miscible gas injection, water-alternating-gas (WAG) injection injection, simultaneous water-and-gas (SWAG) injection, foam-assisted WAG (FAWAG) injection, and microbial EOR (MEOR). Each EOR technology that has been initiated in the North Sea was identified with its respective maturity level and/or maturation time frame, technology use restrictions, and process efficiency on the basis of incremental oil. Apart from WAG at Ekofisk and FAWAG at Snorre central fault block (CFB), all technologies have been applied successfully (i.e., positive in economic terms) to the associated fields. HC miscible gas injection and WAG injection can be considered mature technologies in the North Sea. The most commonly used EOR technology in the North Sea has been WAG, and it is recognized as the most successful EOR technology. The main problems experienced were injectivity (WAG, SWAG, and FAWAG projects), injection system monitoring, and reservoir heterogeneities (HC miscible gas injection, WAG, SWAG, and FAWAG projects). Approximately 63% of all the reported EOR field applications have been initiated on the Norwegian continental shelf (NCS), 32% on the UK continental shelf, and the remainder on the Danish continental shelf. Statoil has been the leader in conducting EOR field applications in the North Sea. The majority of future research will concentrate on microbial processes, CO2 injection, and WAG (including SWAG) injection schemes. In this review, laboratory techniques, global statistics, simulation tools, and economical evaluation were not considered and are considered outside of the scope of this paper.
This paper presents an application of probabilistic, ensemble-based computer-Assisted History Matching (AHM) with uncertainty to Integrated Reservoir Model (IRM) of a Middle Eastern reservoir. The paper outlines the most important characteristics of the AHM workflows for rigorous quantification of model uncertainty, optimization of history matching parameters and execution of large-scale reservoir simulations using Massive Parallel Processing technology. The AHM approach integrates probabilistic Bayesian inference using Ensemble Smoother with Multiple Data Assimilation (ES-MDA), which simultaneously assimilates the data and generates maximum a-posteriori updates of reservoir model parameters in a variance-minimizing update scheme. Variability and sensitivity analyses are conducted to identify the most dominant reservoir parameters and a large number of geo-cellular model realizations is generated to rigorously capture the uncertainty ranges. The AHM workflow was applied to a synthetic Dual-Porosity Dual-Permeability (DPDP) oil reservoir model with approximately (~) 34 million grid-cells. The simulation model span ~50 years of production with flank water injection. The optimization objective was to minimize the joint misfit of watercut, oil-rate and static well pressure in ~50 producing wells and improve well-level history match. An enhancement of AHM workflow is proposed to improve the simulation model connectivity as well as the accuracy of the history match by implementing the streamline-based approach to update fracture network through drainage volume analysis of injector-producer pairs. While the computational performance of the used ES-MDA algorithm was found very robust and fairly independent of the geological and engineering complexity of studied simulation cases, the overall complexity of IRMs can raise memory-allocation, computation and information technology (IT) communication challenges. The paper discusses these challenges and proposes measures to alleviate them for successful deployment of AHM workflows to large-scale models.
Presence of paleo zone, which frequently exists below Free-Water-Level surface, can impact dynamic reconciliation of reservoir simulation models. The process is even more challenging with embedded complex representations of reservoir connectivity (conductive fractures) and inherent uncertainty associated with geological and flow modeling. We present a rigorous approach that integrates characterization of paleo zone, parameterization of paleo zone conductivity and application of flow profiles as a guide in accelerated history matching study of large-scale Dual Porosity-Dual Permeability model. The presence of immobile oil within paleo zone can cause permeability reduction and inherently limit aquifer support to oil zone. Accordingly, such occurrence can be represented as a low permeability streak or region in the simulation model and leveraged for more accurate calibration of model injection wells located inside the paleo zone. We performed probabilistic sensitivity analysis and parameterization of paleo zone conductivity using Design of Experiments on a synthetic simulation model with optimized aquifer size and strength as the basecase. The outcome of the synthetic sensitivity scenarios using dynamic model strongly indicates that paleo zone is partially sealing. Multiple scoping runs were performed to identify appropriate permeability values required to calibrate the model. The use of multipliers in porositypermeability transform reproduces blocking or baffling effect of the paleo zone, considering this fluid will behave as part of the rock framework. Porosity and permeability were recomputed inside the paleo zone based on Bulk Volume of Water (BVW) data assessment. The higher the BVW the higher the chance to have effective communication between the oil leg and aquifer. These multipliers represent the probability of the sealing character of the paleo zone and reflect on the non-uniform distribution of accumulated hydrocarbons. Above methodology was used to define the initial set of paleo zone petrophysical property modifiers, rendering multiple model realizations within optimistic-pessimistic range. Flow profiles can be used to guide segmentation of paleo zone with preferential well injectivity to further improve the efficiency of history matching process. Our paper demonstrates a successful application of multi-variate characterization and modeling of paleo zone geometry and properties for a history match of a conceptual, complex reservoir simulation model under reservoir uncertainty. An innovative approach to probabilistic parameterization of paleo zone conductivity has contributed to a model with exceptionally high quality and rendered a reservoir simulation model with reliable predictive capability in accelerated time.
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