EN 
BR Petroleum exploration activities demand the creation of lots of models to represent the uncertainties of a given reservoir, aiming to guide decision-making regarding exploration strategies. As part of these activities, a data assimilation process may refine the model set and discard models that do not honor the history data of the reservoir, given an error tolerance level. Reservoir engineers may use visualizations to analyze the partial results of iterative data assimilation processes and make decisions such as accepting the models selected in an iteration or refining the model set. Among a set of available visualizations for this process, we did not find in the literature the use of interactive heatmaps based on reorderable matrices. In this paper, we propose to add this kind of visualization to the analysis toolset for data assimilation, aiming to help engineers to better overview data assimilation datasets and how their models honor the history data of a reservoir. The proposed visual mappings use a set of heatmaps that enable the engineers to analyze a misfit measure (NQDS) for a given iteration, regarding models, wells, and their dynamic attributes. Users may reorder and filter these heatmaps according to their needs. We implemented our proposal in a web-based prototype that was submitted to a preliminary evaluation with real users in a synthetic scenario. This evaluation revealed positive opinions about the potential of the tool, which may be used to complement other approaches, and provided some opportunities for improvement.
Author: EPIC Energy
Model-Based Life-Cycle Optimization for Field Development and Management Integrated with Production Facilities
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.
Construction of Single-Porosity and Single-Permeability Models as Low-Fidelity Alternative to Represent Fractured Carbonate Reservoirs Subject to WAG-CO2 Injection Under Uncertainty
Fractured carbonate reservoirs are typically modeled in a system of dual-porosity and dual-permeability (DP/DP), where fractures, vugs, karsts and rock matrix are represented in different domains. The DP/DP modeling allows for a more accurate reservoir description but implies a higher computational cost than
the single-porosity and single-permeability (SP/SP) approach. The time may be a limitation for cases that require many simulations, such as production optimization under uncertainty. This computational cost is more challenging when we couple DPDP models with compositional fluid models, such as in the case of fractured light-oil reservoirs where the production strategy accounts for water-alternating-gas (WAG) injection. In this context, low fidelity models (LFM) can be an interesting alternative for initial studies. This work shows the potential of compositional single-porosity and single-permeability models based on pseudo-properties (SP/SP-P) as LFM applied to a fractured benchmark carbonate reservoir, subject to WAG- CO2 injection and gas recycle. Two workflows are proposed to assist the construction of SP-P models for studies based on (i) nominal approach and (ii) probabilistic approach of reservoir properties. Both workflows begin with a parametrization step, in which the pseudo-properties are optimized for a base case in order to minimize the mismatch between forecasts of the SP/SP-P and DP/DP models. The new parametrization methods proposed in this work showed to be viable for the construction of the SP/SP-P models. For studies under uncertainties, the workflow proposes obtaining pseudo-properties by robust optimizations based on representative models from a DP/DP ensemble, which proved to be an effective method. The case study is the benchmark UNISIM-II-D-CO with an ensemble of 197 DP/DP models and two different production strategies. The risk curves for production, injection and economic indicators obtained from DP/DP and SP/SP-P ensembles showed good match and the computational time spent on simulations of the SP/SP-P ensemble was 81% faster than DP/DP models, on average. Finally, the responses obtained from both ensembles were validated in a reference model (UNISIM-II-R) that represents the true response and is not part of the ensemble. The results indicate the SP/SP-P modeling as a good LFM for preliminary assessments of highly time-consuming studies. Besides, the workflows proposed in this work can be very useful for assisting the construction of SP/SP-P models for different case studies. However, we recommend the use of the high-fidelity models to support the final decision.
Data-driven forecasting of oil & gas production: a recurrent neural networks approach
Our main objective is to develop and compare two data-driven forecasting models based on Recurrent Neural Networks (RNN). We built the models with the Long Short-Term Memory (LSTM) and the Gated Recurrent Units (GRU). The data was obtained from a synthetic benchmark (UNISIM-II-H), which simulates an Oil & Gas (O&G) pre-salt production field. We performed the training/testing procedures: we trained the models with 80% of data from the same well and tested with the remaining data. Forecasting is calculated using as input the historical record of production variables (liquid, oil, gas & pressure). We also measured the symmetric mean absolute percentage error (SMAPE) to compare our forecasting with the data from the selected benchmark. Several experiments were performed; we used 28, 45, and 90 days for historical records, with 7, 15, and 30 days for forecasting, respectively—most of the experiments exhibited and SMAPE lower to 20. Results from the RNN models exhibited relative values compared to the expected data from the benchmark in most experiments for oil & gas production values.
Methodology to optimize the WAG-CO2 injection strategy and injection well ICV control rules in light-oil carbonate reservoirs with pre-salt features
Reservoirs of the pre-salt contain a significate amount of CO2 that should not be emitted into the atmosphere. The WAG-CO2 injection process is an alternative to give an ecologically sustainable destination to the CO2 and can increase oil recovery in the pre-salt fields. The optimization of the WAG-CO2 injection scheme, such as cycle duration, can significantly affect its performance in terms of oil recovery and net present value (NPV), raising the need for good optimization methods. In the face of the high uncertainty that typically exists in these scenarios, Inflow Control Valves (ICV) provide operational flexibility to the production strategy, allowing to manage field injection/production more efficiently. This work proposes a methodology to optimize the injection well control variables since the early stages of field development that considers the condition of total gas reinjection (CO2 and natural gas). The methodology optimizes the
opening phase that each well will start injecting during the ramp-up period of the platform, the cycle duration and the phase, gas or water, that each well will inject in the first WAG-CO 2 bank, and the injection wells ICV control rules. The developed methodology was applied to a benchmark case called UNISIM-II-D, based on Brazilian pre-salt trends. Compared to a based injection strategy, the methodology proved capable of improving field management at minimum added cost, increasing oil recovery and the net present value.
Fundamental aspects, mechanisms and emerging possibilities of CO2 miscible flooding in enhanced oil recovery A review
Capturing carbon dioxide (CO2) at its combustion point and thereby storing it in geological sites or its usage for enhancing oil recovery (EOR) through miscible gas flooding technology aims to mitigate atmospheric/anthro- pogenic CO2 emissions. Injection of CO2 possesses an immense potential for production improvement in matured oil reservoirs. Oil recovery is increased by viscous fluid drive, oil phase swelling and oil viscosity reduction. Miscible CO2 floods diminish interfacial tension (IFT ~ 0) between gas and oil, and alters the wettability. This review discusses the various technical aspects of oil production enhancement via miscible CO2 application with identification of the significant research gaps. The mechanisms of first contact and multiple contact miscibility, techniques of minimum miscibility pressure (MMP) determination (experimental, theoretical and numerical), the influence of CO2 concentration on rock mineralogy and surface roughness with various associated reservoir parametric (pressure, temperature, salinity, etc.), and the mechanisms of oil displacement from laboratory ex- periments to field applications are discussed elaborately. The review also deals with the new approaches of CO2 flooding viz. carbonated water injection, near miscible CO2 flooding, water alternating gas (WAG) injection, CO2 huff ‘n’ puff, and CO2 thickening. Finally, CO2-EOR in carbon capture, utilization and storage (CCUS), the environmental aspects, challenges and future outlooks of CO2 miscible flooding are discussed. Therefore, a re- pository of CO2 miscible EOR is established in this review assisting an enrichment in our current understanding of this topic.
A Random Forest-Assisted Decomposition-Based Evolutionary Algorithm for Multi-Objective Combinatorial Optimization Problems
Many real-world optimization problems involve time-consuming fitness evaluation. To reduce the computational cost of expensive evaluations, researchers have been developing surrogate models to approximate the objective function values of unevaluated candidate solutions. However, most of the research has been developed for continuous optimization problems, while only a few of them address surrogate modeling for expensive multi-objective Combinatorial Optimization Problems (COPs). COPs have inherently different challenges than continuous opti- mization. For example, (i) many COPs have categorical and nom- inal decision variables; (ii) they often require the combination of both global and local search mechanisms; and (iii) some of them have constraints that make them NP-hard problems, which makes them even more difficult to solve with a reasonable number of fitness evaluations. To address these issues, this paper proposes a surrogate-assisted evolutionary algorithm that combines the decomposition-based algorithm MOEA/D, Tabu Local Search, and Random Forest as a surrogate model to approximate the objective function of unevaluated individuals on multi-objective COPs. Experiments were conducted on constrained and uncon- strained well-known multi-objective combinatorial optimization benchmark problems. The experimental results demonstrate that the proposed design outperforms state-of the-art algorithms without violating the restrictions in the number of objective function evaluations, which indicates that it may be suitable for real-world expensive multi-objective COPs.
A diversity preservation method for expensive multi-objective combinatorial optimization problems using Novel-First Tabu Search and MOEA/D
Expensive multi-objective combinatorial optimization problems have constraints in the number of objective function evaluations due to time, financial, or resource restrictions. As most combinatorial problems, they are subject to a high number of duplicated solutions. Given the fact that expensive environments limit the number of objective function evaluations, the existence of duplicated solutions heavily impacts the optimization process due to poor diversity and low convergence speed. This paper proposes the Novel-First Tabu Search, a greedy-strategy mechanism that uses Knowledge-Assisted Local Search methods to preserve the population diversity and increase the exploration and exploitation ability of MOEA/D. Experiments are conducted on constrained, unconstrained, multimodal, deceptive, linear, convex, and non-convex Pareto Front multi-objective combinatorial optimization benchmark problems. This paper also conducts an experiment on the real-world, expensive problem of Well Placement Optimization using a benchmark case based on the Namorado oil field, located in the Campos Basin, Brazil. The experimental results and performance comparison with state-of-the-art algorithms demonstrate that the proposed design significantly preserves diversity and increases convergence without violating the constraint in the number of objective function evaluations.
Iterative sequential robust optimization of quantity and location of wells in field development under subsurface, operational and economic uncertainty
Determination of optimal quantity and location of wells is a crucial step in any field development project. Multiple challenges exist in the real-world projects, such as gigantic multidimensional search space and complex parameterization of the problem. To address these challenges, we present an iterative sequential optimization framework that consists of multiple stages for optimizing number and location of producers and injectors. Once the initial guesses have been established, the first optimization stage begins in order to determine the optimal number of producers and injectors. Next, a two-stage process is used to determine the best location for each well. By optimizing the number and location of wells, well type is optimized implicitly and does not need to be included in the optimization process as an individual decision variable. To consider the dependency and coupling between parameters, the entire optimization process is repeated iteratively up to a stopping point. The proposed approach is highly flexible and can be extended to include other potential decision variables of field development projects. We test the proposed methodology on UNISIM–II–D, a realistic benchmark case that represents pre-salt reservoirs in deep offshore Brazil, which are light-oil fractured carbonates with large volumes of CO2-rich associated gas. In our application, all producers and injectors are equipped with internal control valves (ICVs), all produced gas is reinjected into the reservoir, and water-alternating-gas with CO2 (WAG-CO2) is used to increase oil recovery and ensure that the produced CO2, as a harmful greenhouse gas, can be safely discarded under- ground. We use the iterative discrete Latin hypercube (IDLHC) optimization algorithm. To achieve a robust decision, we consider subsurface, operational and economic uncertainties by considering nine representative reservoir models and three economic scenarios. Based on the results, the proposed framework can provide an appropriate solution containing optimal quantity of wells, their locations and types in realistic cases with large search spaces and complex parameterizations.