Tag: asset and portfolio management

Investigation of Biases Caused by Model-Based Optimization Processes for Reservoir Management

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In reservoir management, many decisions are made considering model-based production forecasts and optimization processes. These approaches can generate biases and the actual production and economic return may be overestimated. One of the reasons for these biases is the optimization process itself (procedure bias). Thus, the objective of this work is to investigate biases caused by model-based optimization processes using synthetic benchmark cases, analyzing the magnitude and the impact on future decisions.

We use synthetic benchmarks composed of: (1) an ensemble of data-assimilated simulation models; (2) a subset of this ensemble, named Representative Models (RMs); (3) a reference case, used as the real response of the reservoir (ground truth). Two case studies are analyzed: one focused on design variables (development phase), and the other on control variables (management phase). We demonstrate how specialized and robust strategies (resulting from nominal and robust optimizations, respectively) behave in relation to the ensemble of models and in relation to the reference case, using Net Present Value (NPV) and Expected Monetary Value (EMV) as objective functions.

The results confirm the presence of bias and overestimated forecasts caused by optimization processes. In Case Study 1 (development phase), the robust strategy showed an expected return improvement of 45% due to optimization, while the actual gain was only 6%. Specialized strategies presented differences between expected and actual economic gains ranging from 38% to 179% (with an average of 79%). In Case Study 2 (management phase), the robust strategy yielded a 4.1% expected increase in economic return compared to a 2.5% actual gain, with specialized strategies showing an average overestimation of 38% for the specialized strategies. The bias was stronger in Case Study 1 due to the greater impact of development variables on reservoir performance. Risk curve and boxplot analyses showed that strategies tend to become overly specialized to the model in which they were optimized, may leading to suboptimal decisions when applied to the real field.

By employing synthetic benchmarks with known reference cases, this work quantifies the overestimation introduced by optimization processes, providing valuable insights to help practitioners recognize and account for procedure bias, reducing the risk of overconfident model-based decisions in real-field applications.

Enhancing Asset Profitability with Flexibility for Life Cycle Field Development – A Comparative Study for Well Placement Allocation and Platform Capacity

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In the context of rising global energy demands that are aligned with sustainable energy supply, making informed decisions regarding investments has become increasingly complex. This complexity is particularly challenging in oil and gas management, where devising a production strategy and commencing field development pose challenges given the multitude of uncertain variables and extended timelines involved. Flexibility is key to address these uncertainties. Hence, the objective of this article is to evaluate the importance and advantages of considering the expected value of flexibility in the decision-making process to create a strategy able to deal with the risks imposed in the petroleum industry. Doing so, this article provides an examination of different approaches employed for the implementation of flexibility, considering the well placement allocations, final strategy selection, and platform capacity, thereby offering an informed perspective on this crucial aspect of reservoir strategic management.

The methodology for the construction of a flexible strategy employs theories in decision analysis combined with reservoir simulation models and optimization methods in a Bayesian probabilistic approach to access the expected value of the flexibility (EVoF). We present a structured technique to assess and select optimal strategies, specifically focusing on managing uncertainty in the initial stages of field development to identify potential platform capacities and drilling location strategies in the face of uncertainties related to reservoir characteristics, facility operations, and market conditions. To illustrate the results, we conduct a case study on an offshore benchmark field with Brazilian pre-salt features under WAG-CO2 recovery method, involving the complete reinjection of produced CO2 to mitigate greenhouse gas effects.

The results reveal that the initial strategy can highly impact the final net present value outcome and risk curves due to the first wells drilled. The results also indicate that increasing flexibility in the early stage of development could extract the best results related to financial return. Our study underscores the immense potential of integrating flexibility valuation and uncertainty quantification into the energy planning and policy-making process. It also highlights that the holistic integration between flexibility and reservoir simulation facilitates the identification of innovative investment strategies and enhances the decision-making process with the tools to navigate the complexities of uncertainty with greater confidence and adaptability.

This innovative approach offers a structured technique that not only addresses uncertainties in the subsurface reservoir and economic scenarios but also contributes to the identification of methodologies for investment management, enhancing the adaptability in the dynamic landscape of reservoir engineering.

Model-Based Petroleum Field Management in Three Stages: Life-Cycle, Short-Term, and Real-Time

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The objective of this work is to present a new practical methodology to manage petroleum fields considering three stages (life-cycle, short-term, and real-time) that can run alongside different model fidelities and characteristics. The model-based field management process follows the general methodology proposed by Schiozer et al. (2019) with four activities: (1) fit-for-purpose models construction, (2) data assimilation for uncertainty reduction, (3) life-cycle production optimization and (4) short-term optimization for real-time implementation. The selection of the production strategy for field management comprehends the last two activities. Life-cycle optimization is the first stage of the process and generates control setpoints for short-term analysis. Short-term optimization is then used to improve the quality of the solutions considering the control parameters of the next cycle (considering a closed-loop procedure). Real-time solution is then implemented considering operational disturbances from real operations. The methodology was applied to a benchmark case (UNISIM-IV-2026) which is a case based on a typical carbonate field from the Brazilian Pre-salt, with light oil and submitted to Water-Alternate-Gas injection with CO2 (WAG-CO2). The results show that the methodology is applicable to real and complex fields. As the three stages can run simultaneously, one can (1) use different model fidelities to improve the quality of the solutions and (2) use model-based solutions for real-time implementation. Life-cycle optimization using complex simulation models and long-term objectives can run in the background to generate control setpoints for short-term analysis in which lower fidelity models and simplified solutions can be used for the control and field revitalization parameters of each closed-loop cycle. Real-time solutions can be implemented considering operational problems and disturbances. This work presents a novel procedure to integrate three stages for production optimization that can run in parallel, allowing the integration of life-cycle and real-time solutions. The methodology (1) allows the use of complex reservoir simulation models from the life-cycle production strategy optimization, (2) focuses short-term control parameters that improve the quality of the short-term solution, and (3) guides real-time implementation, so it can be the basis to a digital field management.