Field Development Process Revealing Uncertainty Assessment Pitfalls

Morosov, André Luís; Schiozer, Denis José

doi:10.2118/180094-ms

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…Silva et al (2017) also presented a closed-loop process for the UNISIM-I-D benchmark with ES-MDA. The main difference from the work of Morosov and Schiozer (2016) was the use of distance-dependent localization. They reported a better response when compared with the reference model.…”

Section: Es-mda and The Localization Techniquementioning

confidence: 96%

“…Avansi and Schiozer, (2015) selected the range of the uncertain parameters based on the knowledge of the Namorado field while avoiding values that are not compatible with the case. Finally, because UNISIM-I-H is a benchmark case and different authors have been using it (Morosov and Schiozer 2016;Silva et al 2017;Soares et al 2018), it is important to keep some basic features of the case in order to validate and compare different methodologies.…”

Section: Application: Unisim-i-hmentioning

confidence: 99%

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A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

Soares

Maschio

Schiozer

2019

J Petrol Explor Prod Technol

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History matching, also known as data assimilation, is an inverse problem with multiple solutions responsible for generating more reliable models for use in decision-making processes. An iterative ensemble-based method (Ensemble Smoother with Multiple Data Assimilation-ES-MDA) has been used to improve the solution of history-matching processes with a technique called distance-dependent localization. In conjunction, ES-MDA and localization can obtain consistent petrophysical images (permeability and porosity). However, the distance-dependent localization technique is not used to update scalar uncertainties, such as relative permeability; therefore, the variability for these properties is excessively reduced, potentially excluding plausible answers. This work presents three approaches to update scalar parameters while increasing the final variability of these uncertainties to better scan the search space. The three approaches that were developed and compared using a benchmark case are: binary correlation coefficient (BCC), based on correlation calculated by ES-MDA through cross-covariance matrix C f MD (BCC-C MD); BCC, based on a correlation coefficient between the objective functions and scalar uncertainties (R) (BCC-R); and full correlation coefficient (FCC). We used the work of Soares et al. (J Pet Sci Eng 169:110-125, 2018) as a base case to compare the approaches because although it showed good matches with geologically consistent petrophysical images, it generated an excessive reduction in the scalar parameters. BCC-C MD presented similar results to the base case, excessively reducing the variability of the scalar uncertainties. BCC-R increased the variability in the scalar parameters, especially for BCC with a higher threshold value. Finally, FCC found many more potential answers in the search space without impairing data matches and production forecast quality.

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Section: Es-mda and The Localization Techniquementioning

confidence: 96%

Section: Application: Unisim-i-hmentioning

confidence: 99%

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

Soares

Maschio

Schiozer

2019

J Petrol Explor Prod Technol

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“…As discussed in Introduction, recent work that applied modified versions of CLFD (Morosov and Schiozer, 2016;Hanea et al, 2017), reported that application of CLFD, in some cases, has resulted in slight reduction of true-model NPV. It may seem counter-intuitive that assimilating new data and re-optimizing decision variables over the remaining span of project life, in cases has lead to a decrease in true-model NPV (which is the ultimate outcome).…”

Section: Trumap: True-model-based Assessment Of Performancementioning

confidence: 99%

“…The CLFD framework has also been applied in recent work by other researchers. Morosov and Schiozer (2016) applied the CLFD to a realistic example, while Hanea et al (2017) applied the CLFD framework in simpler settings where only drilling sequence is optimized. Compared with the original work (Shirangi and Durlofsky, 2015;Shirangi, 2013), they used different reservoir modeling and different model calibration and optimization approaches in their CLFD application.…”

Section: Introductionmentioning

confidence: 99%

Closed-loop field development with multipoint geostatistics and statistical performance assessment

Shirangi

2019

Journal of Computational Physics

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Closed-loop field development (CLFD) optimization is a comprehensive framework for optimal development of subsurface resources. CLFD involves three major steps: 1) optimization of full development plan based on current set of models, 2) drilling new wells and collecting new spatial and temporal (production) data, 3) model calibration based on all data. This process is repeated until the optimal number of wells is drilled. This work introduces a new CLFD implementation for complex systems described by multipoint geostatistics (MPS). Model calibration is accomplished in two steps: conditioning to spatial data by a geostatistical simulation method, and conditioning to production data by optimization-based PCA. A statistical procedure (TruMAP) is presented to assess the performance of CLFD. For performance assessment by TruMAP, the methodology is applied to an oil reservoir example for 25 different true-model cases. Application of a single-step of CLFD, improved the true NPV in 64%-80% of cases. The full CLFD procedure (with three steps) improved the true NPV in 96% of cases, with an average improvement of 37%. These results indicate the effectiveness of performing multiple steps of closed-loop optimization. This massive computational experiment involved about 9.5 million reservoir simulation runs that took about 320,000 CPU hours.

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“…In fact, this paper evolved from other model-based management researches that used the same benchmark case. Morosov and Schiozer (2016), for instance, applied a closed-loop technique in this benchmark consisting of three main steps: data acquisition, history matching and production strategy optimization. These steps were repeated in several cycles using simulation models.…”

Section: Introductionmentioning

confidence: 99%

Investigation of production forecast biases of simulation models in a benchmark case

Botechia

Gaspar

Avansi

et al. 2018

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

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Reservoir management decisions are often based on simulation models and probabilistic approaches. Thus, the response of the model must be sufficiently accurate to base sound decisions on and fast enough to be practical for methodologies requiring many simulation runs. However, simulation models often forecast production rates different to real production rates for various reasons. Two possible causes of these deviations are (1) upscaling (a technique to reduce the computational time of simulation models by reducing the number of grid blocks) and (2) uncertainties (the values established to attributes are different from real values caused by lack of knowledge of real reservoir). Morosov and Schiozer [(2016) applied a closed-loop technique in a benchmark case where decisions taken using the simulation models are applied to a reference case. The optimized production strategy, using simulations models, increased the expected monetary value of the project by about 29%, but the Net Present Value (NPV), calculated using a reference case, decreased by 2%. The real NPV was outside the expected range and revealed that the set of models did not fully represent the real field, even for high-quality history-matched models. The objective of this study is to identify the causes of these discrepancies. To reach this goal, we investigate and analyze both the impact of the upscaling and the uncertainty on production and economic indicators. We use a set of representative models of benchmark UNISIM-I (Avansi and Schiozer, 2015) to consider the effects of uncertainty and upscaling. Our main concern was the uncertainties in the distribution of petrophysical properties that strongly influence the productivity and injectivity of wells, noted by Morosov and Schiozer (2016) as being the main cause for differences among models. Furthermore, to verify the isolated effects of the possible causes of deviation, we use a single model to show only the effects of upscaling, and another set of models showing only the uncertainty. The results showed that the impact of the uncertainties was higher than the upscaling for the studied case. The upscaling generated an optimistic bias for production and economic indicators, but well-correlated with the reference case. The uncertainties significantly affected the production forecasts for this study. This happened because the response of the wells is highly dependent on the petrophysical properties of the model, which varies widely between the different models representing uncertainties and was not adequately depicted by the representative models.

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Field Development Process Revealing Uncertainty Assessment Pitfalls

Cited by 11 publications

References 21 publications

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

Closed-loop field development with multipoint geostatistics and statistical performance assessment

Investigation of production forecast biases of simulation models in a benchmark case

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