Tag: Optimization problem

Optimizing Gas Export Flexibility for Complex Offshore Reservoirs: A Brazilian Case

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Oil and gas reservoir management is associated with uncertainties and risks that can significantly impact performance and economic outcomes. The objective of this work is to present how flexibility can be used to manage risk and uncertainty, as well as evaluate the potential flexibility to export and commercialize natural gas as an alternative to water-alternating-gas (WAG) in Brazilian pre-salt fields, identifying favorable and unfavorable scenarios for its implementation. This work presents a case study that addresses the challenges and opportunities of the expected value of the flexibility associated with natural gas export.

The methodology developed presents a structured technique to assess and select optimal strategies under subsurface uncertainties and possible market fluctuations, combining asset portfolio management with reservoir simulation. One of the main advantages of this methodology is that the chance of success is determined through an automated procedure that can be obtained using the production optimization of representative scenarios. Additionally, to illustrate the applicability, we present an application case study to design flexible facilities that allow future expansion for natural gas commercialization, thus capturing possible upsides considering variations in oil and gas selling prices. We also present how these variations impact the overall design to reduce risks and enhance asset value using a simulation model designed to replicate the Brazilian pre-salt fields and forecasting the value of the natural gas in the country.

The results show that this integrated analysis addresses immediate challenges and highlights future advancement potentials through strategic flexibility in Brazil’s natural gas industry, demonstrating that well-planned flexibility can significantly mitigate risks and enhance the resilience of petroleum management strategies. By aligning sustainable petroleum production with CO2 fraction reinjection, we argue that it is more lucrative to produce the natural gas fraction at lower oil prices and that there is a balance point of WAG miscibility to gas price, coupled with enhanced flexibility. We demonstrate how it is possible to increase asset value and mitigate risks, therefore addressing a major concern for stakeholders.

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.

Fast Objective Function Estimator Based on Parametric Dynamic Mode Decomposition for Wag-Co2 Injection in Carbonate Reservoirs

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Objective/Scope

Fast-objective function estimators (FOFE) are often used to speed up reservoir management. This work presents a FOFE constructed with the parametric Dynamic Mode Decomposition (DMDp) method for a carbonate reservoir with WAG-CO2 injection. The FOFE results are then compared to simulation results to analyze the FOFE’s efficiency.

Method/Procedure/Process

We present an example of how changes in the production strategy can affect reservoir behavior. The FOFE utilizes snapshots of gas and water saturation of numerical simulation runs with different sizes of WAG-CO2 cycles to predict the snapshots and fluid rates of a production strategy with a desired WAG-CO2 cycle size. The FOFE utilizes the DMDp method to calculate the saturation snapshots and material balance equations to calculate oil, water, and gas rates. Unlike the standard where snapshots are stacked up for multiple parameters, leading to increased computational costs, here we perform interpolation directly on the reduced Koopman operator. This leads to enhanced performance as the time eigenvalues are no longer shared between all parameters. The case study is the public access benchmark UNΊSFM-ΓV-2022, a carbonate reservoir model with characteristics of the Brazilian pre-salt. This model represents a developed reservoir with a WAG-CO2 recovery method for a compositional simulator with historical data.

Results/Observations/Conclusions

For this work, the FOFE utilizes snapshots of two reservoir simulations, one with a WAG-CO2 cycle size of 6 months and the other with 18 months, to predict the states of a production strategy with 12 months of WAG-CO2 cycle. The FOFE results of gas, oil, and water are compared to a simulation result with the same production strategy. The comparisons for fluid dynamics are shown for reservoir conditions, and their curves with relative differences are provided. The FOFE can predict the states of a different field scenario, dispensing the necessity of extra numerical simulation runs. This result is promising for production optimization problems which require a significant amount of simulation runs to incorporate the many reservoir uncertainties, as it is observed in highly heterogeneous carbonate reservoirs.

Novel/Additive Information

The innovation of this work is the utilization of the DMDp in a highly heterogeneous reservoir with three-phase flow and WAG-CO2 injection utilizing commercial software. This FOFE can be utilized to reduce the time and computational effort necessary for the decision-making process involving the control variable of WAG-CO2 cycle size.

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.

Model-Based Life-Cycle Optimization for Field Development and Management Integrated with Production Facilities

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Reservoir simulation models often support decision making in the development and management of petroleum fields. The process is complex, sometimes treated subjectively, and many methods and parameterization techniques are available. When added to uncertainties, the lack of standardized procedures may yield largely suboptimal decisions. In this work, we present a comprehensive outline for model-based life-cycle production optimization problems, establishing guidelines to make the process less subjective. Based on several applications and a literature review, we established a consistent methodology by defining seven elements of the process: (1) the degree of fidelity of reservoir models; (2) objective function (single- or multi-objective, nominal or probabilistic); (3) integration between reservoir and production facilities (boundary conditions, IPM); (4) parametrization (design, control and revitalization optimization variables); (5) monitoring variables (for search space reduction); (6) optimization method, including optimizer/ algorithm, search space exploration, faster-objective function estimators (coarse models, emulators, others), type of ensemble-based optimization (robust or nominal based on representative models); (7) additional improvements (value of information and flexibility). With an application on a publicly available benchmark reservoir, this work shows how a model-based life-cycle optimization process can be systematically defined. In this initial work, the focus is the field development phase and some simplifications were made due to the high computational demand, but in future works we plan to address the control and revitalization variables and reduce the number of simplifications to compare. The optimization results are analyzed to understand the evolution of the objective function and the evolution of the optimization variables. We also discuss the importance of including uncertainties in the process and we discuss future work to emphasize the difference between life-cycle (control rules) and short-term (effective control) management of equipment, as well as ways to deal with the computational intensity of the problem, such as the combined use of representative models and fast simulation models.

Selection of Representative Scenarios Using Multiple Simulation Outputs for Robust Well Placement Optimization in Greenfields

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In greenfield projects, robust well placement optimization under different scenarios of uncertainty technically requires hundreds to thousands of evaluations to be processed by a flow simulator. However, the simulation process for so many evaluations can be computationally expensive. Hence, simulation runs are generally applied over a small subset of scenarios called representative scenarios (RS) approximately showing the statistical features of the full ensemble. In this work, we evaluated two workflows for robust well placement optimization using the selection of (1) representative geostatistical realizations (RGR) under geological uncertainties (Workflow A), and (2) representative (simulation) models (RM) under the combination of geological and reservoir (dynamic) uncertainties (Workflow B). In both workflows, an existing RS selection technique was used by measuring the mismatches between the cumulative distribution
of multiple simulation outputs from the subset and the full ensemble. We applied the Iterative Discretized Latin Hypercube (IDLHC) to optimize the well placements using the RS sets selected from each workflow and maximizing the expected monetary value (EMV) as the objective function. We evaluated the workflows in terms of (1) representativeness of the RS in different production strategies, (2) quality of the defined robust strategies, and (3) computational costs. To obtain and validate the results, we employed the synthetic UNISIM-II-D-BO benchmark case with uncertain variables and the reference fine- grid model, UNISIM-II-R, which works as a real case. This work investigated the overall impacts of the robust well placement optimization workflows considering uncertain scenarios and application on the reference model. Additionally, we highlighted and evaluated the importance of geological and dynamic uncertainties in the RS selection for efficient robust well placement optimization.