Application of Assisted Optimization to Aid Oil Exploitation Strategy Selection for Offshore Fields

Gaspar, Ana Teresa; Barreto, Carlos Eduardo; Mazo, Eduin Orlando Muñoz; Schiozer, Denis José

doi:10.2118/169464-ms

Cited by 21 publications

(10 citation statements)

References 21 publications

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“…Next, each RM is optimized by changing only the position of undrilled wells using the DECE algorithm from CMG's CMOST. This heuristic criterion simplifies the optimization process greatly increasing NPV with relatively little computational effort (Gaspar et al, 2014). After all RMs are optimized, nine new production strategies are obtained (PS1 n to …”

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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“…The first production strategy is selected using an optimization procedure (Gaspar et al, 2014(Gaspar et al, , 2016aRavagnani et al, 2011;von Hohendorff Filho et al, 2016). 7.…”

Section: Blue Stepsmentioning

confidence: 99%

Model-based decision analysis applied to petroleum field development and management

Schiozer

Santos

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.

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“…In order to optimize the Base1 production strategy, it was considered as optimization parameters the decision (G1) and operation (G2) variables related to UNISIM-I-D definition (Gaspar et al, 2014). The G1 variables were: number, type (vertical or horizontal), location and schedule of wells and capacities constraints (liquid, oil and water production and water injection) of platform.…”

Section: Figure 3-water Production For the 4 Initial Wells With Histomentioning

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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Application of Assisted Optimization to Aid Oil Exploitation Strategy Selection for Offshore Fields

Cited by 21 publications

References 21 publications

Field Development Process Revealing Uncertainty Assessment Pitfalls

Field Development Process Revealing Uncertainty Assessment Pitfalls

Model-based decision analysis applied to petroleum field development and management

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

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