Tag: structural geology

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.

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.

Estimating Rock Typing in Uncored Wells Using Machine Learning Techniques for Brazilian Pre-Salt Carbonate Reservoir

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Accurate rock typing in uncored wells is essential for enhancing reservoir models, particularly in complex geological formations like the Brazilian pre-salt carbonate reservoirs. This study explores the application of machine learning (ML) techniques to estimate rock types in uncored wells. The research leveraged core data from 11 cored wells to calculate the Rock Quality Index (RQI) and Flow Zone Indicator (FZI), identifying 14 distinct rock types through the Discrete Rock Typing (DRT) method, along with well log data such as gamma ray, density, neutron, permeability, and porosity. Various machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Gradient Boosting, Naive Bayes, XGBoost, and Random Forest were tested, where XGBoost achieved the highest accuracy of 73.3%. Applying XGBoost to all wells resulted in accuracy ranging from 0.6 to 0.91 and the model was subsequently used to estimate rock types in over 20 uncored wells to generate reservoir simulation models. This study highlights the efficacy of machine learning (particularly XGBoost) in addressing reservoir complexities and offering significant improvements in the understanding and development of carbonate reservoirs.

Integrated Multi-Scale Pore Characterization of Carbonate Rocks in the Barra Velha Formation, Santos Basin, Brazil

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Carbonate rocks feature heterogeneous porous systems that span multiple scales, from pore level to the reservoir scale. The complexity and diversity of carbonate reservoirs demand a consistent approach to their characterization. The efficient integration of multiscale imaging data and petrophysical data is increasingly important to address the challenges associated with these complex carbonate reservoirs. A crucial step in overcoming these scale gaps in reservoir modeling and simulation involves enhancing the characterization of reservoir flow units and their associations with geological and petrophysical heterogeneities at varying scales. In this study, we focus on the classification of pore types using digital rock analysis and petrophysical evaluation of pre-salt lacustrine carbonates from the Barra Velha Formation (BVF) in the Santos Basin using computerized tomography (CT), core samples description, and petrography. Eight types of pores were identified at the core scale: interparticle, stratiform-vuggy, growth framework, vuggy, vuggy-fracture, fracture, interclast, and intraclast. The distribution and characteristics of these pore types were analyzed at different scales, including thin-sections and micro-CT, and nuclear magnetic resonance (NMR), which highlights the diversity in the porous system and the impact of different pore types on porosity and permeability. NMR analyses illustrated the pore size heterogeneity to provide distinction between tight and porous samples. Hydraulic rock units (HRUs) were defined based on flow zone indicator (FZI) using the probability plot approach. Seven HRUs were defined: HRU1 and HRU2 represent samples with the highest FZI and rock quality index (RQI) values, whereas HRU3 and HRU4 denote intermediate values. HRU5, HRU6, and HRU7 represent units with the lowest values. HRU1 and HRU2 were predominantly associated with vuggy, growth framework, and interparticle porosities, which are often enhanced by dissolution processes. Conversely, HRUs with reduced reservoir qualities (5, 6, and 7), characterized by the lowest permeability values, are more prevalent in intervals with higher silicification and silica and dolomite cementation, presenting a variety of pore types at a macroscale. The integration of multiscale imaging techniques and petrophysical data underscores the complexity of pore systems, providing crucial insights into their reservoir characteristics.