Use of representative models in the integration of risk analysis and production strategy definition

Schiozer, Denis José; Ligero, Eliana Luci; Suslick, Saul B.; Costa, Ana Flávia; Santos, J.A.M.

doi:10.1016/j.petrol.2004.02.010

Cited by 61 publications

(33 citation statements)

References 10 publications

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“…To optimize PS n-1 , a subset of nine Representative Models (RM) are extracted from the M n set. RMs are widely used (Schiozer et al, 2004;Armstrong et al, 2013;Sarma et al, 2013;Marques et al, 2013;Shirangi and Durlofsky, 2015) to reduce the number of models for optimization. The criteria and the algorithm used for the selection are fully described by Meira et al (2015).…”

Section: Methodsmentioning

confidence: 99%

Field Development Process Revealing Uncertainty Assessment Pitfalls

Morosov

Schiozer

2016

SPE Europec Featured at 78th EAGE Conference and Exhibition

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The amount of information available for field development planning is limited, forcing the Production Strategy to be designed under great amounts of uncertainty. During its implementation, new information allows the adaptation of the strategy for economic gain. This work reproduces the field development process under geological uncertainty in case study UNISIM-I-D (benchmark case based on Namorado Field in Brazil). The main objectives are to evaluate the process and observe the evolution of risk curves, all in a controlled environment with real field features.The methodology generates new geostatistical images based on new well logs, assimilates production data using an ensemble-based method and re-optimizes the production strategy using a hybrid algorithm. The field development is carried out by repeatedly applying this framework under human supervision. Each step is customized using algorithms to simplify the implementation and to reduce computational effort, making this methodology more appealing for practical use. New data are collected from a high-resolution reference model that does not belong to the ensemble of models. The process starts with a production strategy, previously optimized under the uncertainties of the case study, which yields the real economic outcome within the original uncertain range.Results show high quality history matching that excessively reduced the risk range and the variability of the updated model sets. Optimizations on the production strategy, based on the updated ensembles, consistently increased the expected monetary value of the project without guaranteeing an increment in the real NPV. Applying the methodology repeatedly throughout the field development increased the EMV by 29% (from 1.532 to 1.975 billion USD) while the real NPV decreased 2% (from 1.346 to 1.319 billion USD), falling out of the expected range and revealing that the model sets did not fully represent the real field. The lack of good representation is aggravated by heterogeneities inherent to the unknown reservoir, which are difficult to identify using only well logs and production data.The results from the application of a closed-loop reservoir development process in a controlled environment warn against similar hidden mechanisms happening on a real field development under similar circumstances. They reveal intrinsic pitfalls in reservoir modeling that may contribute to production forecast problems and call for a reflection on how reservoir uncertainty assessment is performed. We prove that large sets of models do not guarantee coverage of geologic uncertainties as the field development process naturally changes the risk curves.

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Section: Methodsmentioning

confidence: 99%

Field Development Process Revealing Uncertainty Assessment Pitfalls

Morosov

Schiozer

2016

SPE Europec Featured at 78th EAGE Conference and Exhibition

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“…8. Selection of Representative Models (RM) (Schiozer, 2004;Costa, A.P. et al, 2008) based on all objective functions and input variables.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, it is necessary to verify other possibilities, especially for scenarios that are different from Base1. We then recommend selecting some representative models (RM) (Schiozer et al, 2004) and check other strategies that are good for other scenarios.…”

Section: Figure 4 -N P W P Npv and Orf For The 214 Models And Selmentioning

confidence: 99%

Integrated Model Based Decision Analysis in Twelve Steps Applied to Petroleum Fields Development and Management

2015

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This work describes a new methodology based on 12 steps for integrated decision analysis related to petroleum fields development and management considering reservoir simulation, risk analysis, history matching, uncertainty reduction techniques, representative models and selection of production strategy under uncertainty. The example of application is the field UNISIM-I-D which is a benchmark case based on Namorado field, Campos Basin, in Brazil. The main focus of the results is to show that the method can be used in practical applications, i.e., complex reservoirs in different field stages (development and management) because it allows the integration of static (geostatistical images generated by reservoir information) and dynamic data (well production and pressure) to reduce uncertainties allowing risk analysis integrating geological, economic and other uncertainties yielding a decision analysis based on risk-return techniques. In this methodology, no proxy model is used so reservoir simulation is used directly to reproduce field performance. We also show that the methodology is efficient and easy to use, even in complex cases where the computational time is an important concern and in real time operations.

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“…The procedure treats each set of global property at a time and it consists of building hundreds of models through the derivative tree technique (Schiozer et al, 2004), where all the levels of one set of parameters are combined. Then, the quadratic error between each well production curve of each model and history data is computed according to the equation:…”

Section: Methodsmentioning

confidence: 99%

Local History Matching Using 4D Seismic Data and Multiple Models Combination

Davólio

Maschio

Schiozer

2013

EAGE Annual Conference &Amp; Exhibition Incorporating SPE Europec

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Nowadays there are several methodologies to incorporate quantitatively 4D seismic data in the history matching of reservoir simulation models. Most of them add a map attribute derived from 4D seismic, which can be impedance maps, saturations etc., in the objective function of the optimization process. In these cases, the goal is to match the dynamic information provided by 4D seismic with simulation response in the whole reservoir area, or the whole area covered by seismic data. In this work, it is proposed a history matching methodology that uses 4D seismic information locally for each injector (and the associated producers) individually, trying to match the water front movement related to each injector with the observed dynamic changes. In order to guarantee consistency with geology, the matching is done by combining static properties that comes from different simulation models. These models are generated through a combination of the most important uncertain parameters in an uncertainty analysis procedure, geologically consistent, that should be run previously. Then, the matching procedure is based on the computation of the water saturation errors at some sub-regions defined around the injector for multiple scenarios; the error is computed between water saturation derived from 4D seismic and each model simulation result. The models which presented the smallest error for each sub-region are selected and a new simulation model is built by combining the static properties extracted from the selected models at each sub-region. Before calculating the saturation errors the volume of water computed from 4D seismic is calibrated to the volume of injected water providing a more robust calibration. The methodology was applied to a synthetic case where porosity and permeability were the main uncertainties updated by the methodology proposed. Since these properties were generated through geostatistic realizations, the adjusted model kept the geological features. The promising results observed in the synthetic case showed that the methodology can be a good alternative for history matching, since it is an easy to implement procedure and it does not require sophisticated optimization algorithms to incorporate 4D seismic into the process.

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Use of representative models in the integration of risk analysis and production strategy definition

Cited by 61 publications

References 10 publications

Field Development Process Revealing Uncertainty Assessment Pitfalls

Field Development Process Revealing Uncertainty Assessment Pitfalls

Integrated Model Based Decision Analysis in Twelve Steps Applied to Petroleum Fields Development and Management

Local History Matching Using 4D Seismic Data and Multiple Models Combination

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