Mês: setembro 2022

by Gabriel Paulo Turato, João Gabriel Nogueira Duarte de Oliveira, Antonio Alberto S. Santos, Denis José Schiozer, Celmar Guimarães da Silva, presented at 16th International Conference and Computer Graphics, Visualization, Computer Vision and Image Processing 2022, July 2022.

Abstract

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.

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by Denis J. Schiozer, Susana M. Santos, Antonio Alberto S. Santos, João C. von Hohendorff Filho, presented at SPE EuropEC – Europe Energy Conference featured at the 83rd EAGE Annual Conference & Exhibition, June 2022.

Abstract

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.

DOI: https://doi.org/10.2118/209681-MS

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by Davi Eber Sanches de Menezes, Susana Margarida da Graça Santos, Antonio Alberto de Souza dos Santos, João Carlos von Hohendorff Filho, Denis José Schiozer, presented at SPE EuropEC – Europe Energy Conference featured at the 83rd EAGE Annual Conference & Exhibition, June 2022.

Abstract

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.

DOI: https://doi.org/10.2118/209692-MS

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by VictorEduardo Martinez Abaunza, Anderon de Rezende Rocha, presented at Rio Oil & Gas Expo and Conference 2022, September 2022.

Abstract

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.

by Guilherme Nunes Lopes, Susana Margarida da Graça Santos, Denis José Schiozer, Randerson Araújo de Lemos, presented at Rio Oil & Gas Expo and Conference 2022, September 2022.

Abstract

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.