Assisted History Matching Benchmarking: Design of Experiments-based Techniques

Bhark, Eric; Dehghani, K.

doi:10.2118/170690-ms

Cited by 40 publications

(8 citation statements)

References 39 publications

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“…A complete workflow design for history matching and selection of prediction candidates built on standard experimental design techniques coupled with several data analysis methods is described in Bhark and Dehghani (2014). An ensemble-based approach for history matching and uncertainty quantification is described in Hanea et al (2015) to support "fast model updates" as part of a closed-loop workflow design.…”

Section: Workflow Design Modeling Phases and Methodologymentioning

confidence: 99%

Integrated modeling of a complex oil rim development scenario under subsurface uncertainty

Elharith

Huey

Tewari

et al. 2019

J Petrol Explor Prod Technol

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Evaluation of the economic benefit of field development projects in the oil and gas industry is naturally subject to subsurface uncertainties and risks. While high economic returns from produced volumes compensated for the lack of robust uncertainty estimates in the past, lower margins in tight market conditions and more complex project designs require increased efforts in delivering reliable uncertainty estimates for improving reservoir management decision support in times to come. An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Estimation and reduction of prediction uncertainty has no value unless it can potentially influence a decision process. This requires an integrated workflow design across disciplines and a proper risk mitigation plan. This work presents the implementation of an uncertainty workflow in reservoir management with a focus on reservoir modeling and simulation. Practical workflow design steps and intermediate milestones are discussed for generating alternative reservoir model realizations including historical production data. Calibrated model realizations define the basis for prediction estimates of a predefined field development plan. Practical steps are discussed with application to a complex oil rim development for an existing reservoir with long production history. This study aimed at an uncertainty assessment of a field development plan and a proposal for optimized relocations of infill wells considering multiple geological realizations. In conclusion, this work describes examples, experiences, and recommendations for an industry implementation for a risk assessment of a field development plan under subsurface uncertainties.

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Section: Workflow Design Modeling Phases and Methodologymentioning

confidence: 99%

Integrated modeling of a complex oil rim development scenario under subsurface uncertainty

Elharith

Huey

Tewari

et al. 2019

J Petrol Explor Prod Technol

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“…5a. The PDFs and CDFs in these two figures are constructed from the prior and posterior sample sets S 0 andS j ( j ¼ 1; Á Á Á ; l d ) with the kernel density estimate method (Bowman and Azzalini 1997). It is clear from Fig.…”

Section: Methodsmentioning

confidence: 99%

Proxy-Based Work Flow for a Priori Evaluation of Data-Acquisition Programs

Xie

Sarma

et al. 2016

SPE Journal

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Data-acquisition programs, such as surveillance and pilot, play an important role in reservoir management, and are crucial for minimizing subsurface risks and improving decision quality. Optimal design of the data-acquisition plan requires predicting the performance (e.g., in terms of the expected amount of uncertainty reduction in an objective function) of a given design before it is implemented. Because the data from the acquisition program are uncertain at the time of the analysis, multiple history-matching runs are required for different plausible realizations of the observed data to evaluate the expected effectiveness of the program in reducing uncertainty. As such, the computational cost may be prohibitive because the number of reservoir simulations needed for the multiple history-matching runs would be substantial. This paper proposes a framework on the basis of proxies and rejection sampling (filtering) to perform the multiple historymatching runs with a manageable number of reservoir simulations. The work flow proposed does not depend on the linear Gaussian assumption that is a common, yet questionable, assumption in existing methods. The work flow also enables both qualitative and quantitative analysis of a surveillance plan. Qualitatively, heavy-hitter alignment analysis for the objective function and the observed data provides actionable measures for screening different surveillance designs. Quantitatively, the evaluation of expected uncertainty reduction from different surveillance plans allows for optimal design and selection of surveillance plans.

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“…When applied in uncertainty quantification of reservoir production forecast, design of experiments techniques are used to build a proxy model that approximate the relationship between unknown parameter values and misfit function (Castellini et al 2004;King et al 2005;Osterloh 2008;Bhark et al 2014). Space-filling designs, such as Latin Hypercube sampling have been widely used for sampling the multi-dimensional simulation factor space of reservoir simulation experiments.…”

Section: Introductionmentioning

confidence: 99%

“…The parameter values that do not honor the production history are discarded. The main challenge of design of experiments-based approaches is that due to limited number of training points, the constructed proxy model may not be accurate enough for the purpose of Monte Carlo sampling to identify satisfying parameter values (Bhark et al 2014). The second challenge is due to the curse of dimensionality; exhaustive sampling of the proxy model is not a trivial task in a large multiple-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Practical Workflow for Probabilistic Forecasting of Brown Fields Constrained by Historical Data

Yang¹,

Nghiem²,

Erdle³

et al. 2015

SPE Annual Technical Conference and Exhibition

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Brown fields are fields with significant production history. Probabilistic forecasting for brown fields requires multiple history-matched models that are conditioned to available field production data. This paper presents a systematic and practical workflow to generate an ensemble of simulation models that is able to capture uncertainties in forecasts, while honoring the observed production data.The proposed workflow employs the Bayes theorem to define a posterior Probability Density Function (PDF) that represents model forecast uncertainty by incorporating the misfit between simulation results and measured production data. Previous workflows use the Markov Chain Monte Carlo (MCMC) sampling method, which requires an extremely large number (thousands) of simulation runs. To alleviate this drawback, a Proxy-based Acceptance-Rejection (PAR) sampling method is developed in this study to generate representative simulation models that characterize the posterior PDF using hundreds of simulation runs. The proposed workflow can be summarized in five key steps:1. Run an initial set of reservoir simulations by simultaneously varying multiple uncertain parameters using an experimental design method. 2. Construct a proxy function using a Radial-Basis Function (RBF) neural network to approximate the posterior PDF calculated from the initial set of simulation results. 3. Sample the posterior PDF using the PAR sampling method and run an ensemble of new simulation models using the sample values. 4. The new simulation results obtained in step 3 are added to the training datasets generated in step 1 to improve the accuracy of the proxy function. Steps 3 and 4 are repeated until a predefined stop criterion is satisfied. 5. Filter all simulation runs generated in step 3 using appropriate tolerance criteria for various history-match quality indicators. The selected filtered cases constitute the final ensemble of simulation models that can be further used for uncertainty quantification in forecasting.The proposed workflow is demonstrated on two reservoir simulation models:1. Synthetic case. The forecast uncertainty of the ninth SPE Comparative Solution Project (SPE9) simulation model is investigated with a synthetic production history and 32 uncertain parameters. 2. Unconventional oil field case. The workflow is applied to an Eagle Ford shale oil well to determine P90 (conservative), P50 (most likely), and P10 (optimistic) cases of estimated ultimate recovery (EUR).Results of the workflow are compared to those obtained using the Metropolis-Hasting MCMC sampling method. The comparison shows that the proposed workflow only requires 800 simulation runs to obtain results as accurate as the MCMC method with 8000 simulation runs. This translates into a 10times speedup, which makes the proposed workflow practical for many real reservoir simulation studies.

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Assisted History Matching Benchmarking: Design of Experiments-based Techniques

Cited by 40 publications

References 39 publications

Integrated modeling of a complex oil rim development scenario under subsurface uncertainty

Integrated modeling of a complex oil rim development scenario under subsurface uncertainty

Proxy-Based Work Flow for a Priori Evaluation of Data-Acquisition Programs

An Efficient and Practical Workflow for Probabilistic Forecasting of Brown Fields Constrained by Historical Data

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