Uncertainty Quantification Workflow for Mature Oil Fields: Combining Experimental Design Techniques and Different Response Surface Models

Ahmed, Syed Jawwad; Recham, R.; Nozari, Ali; Bughio, Shamsudin; Schulze-Riegert, Ralf; Salem, Ryadh Ben

doi:10.2118/164142-ms

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Appendix A briefly presents the equations for a two-phase immiscible fluid (oil/ water) system. Additional details may be found in the related standard textbooks, such as those by Ertekin et al (2001) and Aziz and Settari (1979). Although our discussion and numerical examples in this paper are based on two-phase fluid-flow systems, the formulation of the flow-network model is general and can be extended to multiphase-fluid-flow systems.…”

Section: Governing Flow-network-model Equationsmentioning

confidence: 99%

Efficient Production Optimization With Flow-Network Models

2014

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Automating model calibration and production optimization is computationally demanding because of the intensive multiphaseflow-simulation runs that are needed to predict the response of real reservoirs under proposed changes in model inputs. Fast surrogate models have been proposed to speed up reservoir-response predictions without compromising accuracy. Surrogate models either are derived by preserving the physics of the involved processes (e.g., mass balance) to provide reliable long-range predictions or are developed solely on the basis of statistical input/ output relations, in which case they can only provide short-range predictions because of the absence of the physical processes that govern the long-term behavior of the reservoir. We present an alternative approach that combines the advantages of both statistics-based and physics-based methods by reducing the flow predictions in complex 3D models to a 1D flow-network model. The existing injection/production wells in the original model form the nodes or vertices of the flow network. Each pair of wells (nodes) in the flow network is connected by use of a 1D numerical simulation model, resulting in a connected network of 1D gridbased simulation models. The coupling between the individual 1D flow models is enforced at the nodes where network edges intersect. The proposed flow-network model provides a useful and fast tool for characterizing interwell connectivity, estimating drainage volume between each pair of wells, and predicting reservoir production over an extended period of time for optimization purposes. The parameters of the flow-network model are estimated by a robust training approach to ensure that the network model reproduces the response of the full model under a wide range of development strategies. This step helps the network model to preserve its predictive power during optimization iterations when alternative development strategies are proposed and evaluated to find the solution. We demonstrate the effectiveness and applicability of the proposed flow-network model by use of two-phase waterflooding experiments.

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Section: Governing Flow-network-model Equationsmentioning

confidence: 99%

Efficient Production Optimization With Flow-Network Models

2014

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An Effective Approach for Uncertainty Analysis and Workflow Optimization in Undeveloped Reservoir

Buzdar

Ali

Hussain

et al. 2016

Day 2 Tue, November 08, 2016

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Subsurface appraisal activities are employed to reduce reservoir uncertainties. Field appraisal/delineation is crucial to understand the subsurface complexity and define the extent of reservoir. In such scenarios, the ability to appropriately characterize reservoir is a primary factor that determines economic viability of a development project. The high cost associated with appraisals of petroleum fields requires manageable risk and uncertainty quantification. Often, results from appraisal campaigns are not enough to reasonably justify decision of developing the field. An efficient methodology for quantification of subsurface uncertainties during the appraisal stage was implemented to frame the objectives for achieving optimum outcomes. The approach is more systematic in comparison to classical methods to gain better understanding of uncertainty parameters, capture level of uncertainty and identify optimal model for the field development. Production forecast using probabilistic approach provides good estimate of cumulative oil or recovery factor considering the impact of uncertainties and risk. This study employs uncertainty quantification and optimization workflow for assisted history matching and forecast during appraisal stage. Uncertainty parameters with ranges and distribution, response parameters along with criteria set were framed into the workflows of sensitivity, history matching and uncertainty analysis. Experimental design techniques (OVAT and Latin Hypercube) were used for sensitivity analysis and uncertainty parameter reduction. Objectives set for scope of studies were driven with detailed analysis of the simulation runs and successfully culminated the promising results within specified project time duration. The methodology is applied to onshore field in the Middle East during the appraisal phase with limited wells.

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Risk Mitigation for Further Development of a Mature Field through Multiple Parameter Sensitivity Study

Yamada

Yamamoto

Alqubaisi

et al. 2018

Day 1 Mon, November 12, 2018

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Reservoir simulation is widely used for field development planning in many fields and the evaluation of uncertainty range in production forecast is indispensable to make decision for further investment. Reservoir simulation model consists of geological, petrophysical and reservoir engineering parameters for each cell and cell boundary. These reservoir model parameters are usually defined based on limited available data in consideration of their uncertainty range. Therefore, the identification of influential parameters and the reduction of uncertainty range for these parameters are key components to mitigate the prediction uncertainty. An Upper Jurassic carbonate reservoir in Field A located in offshore Abu Dhabi has long production history for more than 30 years. Field A experienced several development schemes including natural depletion, crestal gas injection and crestal water injection. The current reservoir simulation model reasonably replicates historical performance on pressure, water cut evolution and GOR trend in field and well-by-well scales. On the other hand, we identified some reservoir model parameters have high uncertainty due to reservoir complexity and lack of reliable data. In this study, we focused on the identification of influential parameters on production forecast and the reduction of parameter uncertainty range using an experimental design approach. More than 200 simulation cases were generated with different combination of selected parameters using Latin Hypercube Sampling method. In each case, we evaluated history matching quality in field scale and relationship between history matching quality and each parameter. We found some parameters have correlation with history matching quality independently from the other parameters settings. This means that the uncertain range of those parameters can be reduced to achieve an acceptable history match irrespective of the other parameters. Furthermore, the prediction uncertain range was analyzed using the selected cases showing reasonable history matching quality to investigate the relationship between cumulative oil production and each parameter. The results indicated some parameters have a stronger impact on production forecast and their uncertainty range need to be reduced by further data gathering or considering other mitigation plans. This study successfully demonstrated that the proposed multiple parameter sensitivity analysis by effective use of experimental design approach enables to reduce the parameter uncertain range and identify the key influential parameters. Furthermore, this study result contributes to the prioritization and optimization of future data gathering plan in Field A.

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Uncertainty Quantification Workflow for Mature Oil Fields: Combining Experimental Design Techniques and Different Response Surface Models

Cited by 5 publications

References 13 publications

Efficient Production Optimization With Flow-Network Models

Efficient Production Optimization With Flow-Network Models

An Effective Approach for Uncertainty Analysis and Workflow Optimization in Undeveloped Reservoir

Risk Mitigation for Further Development of a Mature Field through Multiple Parameter Sensitivity Study

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