This paper presents a novel-methodology to compensate for the poor characterization of high-permeability structures (excess-K: vugs, karsts and super-K features), and non-fault-related-fractures, in naturally fractured Brazilian Pre-Salt carbonate reservoirs. These heterogeneities are often undetectable in well logs and seismic data, but significantly impact well performance. The methodology aims to enhance the representation of such features within dynamic simulation models, improving reservoir characterization and supporting more reliable data-assimilation and forecasting processes. The methodology involves: (1) upscaling high-fidelity fine-grid models to coarser-grids while preserving dynamic behavior, (2) identifying wells with productivity/injectivity mismatches due to a poor excess-K characterization, (3) applying a data assimilation (DA) process to minimize the mismatch between modeled and measured wells production and injection rates by updating the absolute permeability of the matrix. The novelty of the process is that the permeability field is updated by creating a mask (3D property) built by kriging permeability increments estimated from the well cells with productivity/injectivity issues. Therefore, the DA aims to find the least increments of permeability needed for each well such that when this mask is summed with the matrix permeability field all wells present good productivity/injectivity matching with history data. The methodology was applied to a dual-porosity/dual-permeability (DP/DK) compositional reservoir model. Two distinct well behaviors were observed: (1) wells located within fracture zone (12 of 33) showed good productivity/injectivity alignment with historical data and (2) the remaining 21 wells, located away from fracture zone, exhibited significantly poorer productivity/injectivity. This mismatch was attributed to the absence of excess-K features in the original matrix permeability model (Km-field). The optimization process was applied to these 21 wells. For each well a specific Ki-value was settled, defining input-points for kriging. The resulting kriged permeability correction volume (mask) was summed with the Km-field to generate an updated-permeability model. This process was repeated until all wells presented good productivity/injectivity matching with historical data. The process not only corrected the simulated dynamic responses, but also revealed key spatial permeability patterns that had not been captured in the static model. The results served as feedback to the geologists and enabled iterative improvement of the geological model, supporting a more integrated and realistic characterization. Overall, the results validate the methodology as a robust tool for incorporating unresolved high-permeability features in reservoir simulation and improving the quality of data assimilation. This study introduces an automated, iterative probabilistic data-assimilation framework that directly integrates geostatistical kriging with permeability adjustments for excess-Kstructures. The approach provides bidirectional feedback to geological modeling and allows the generation of realistic ensembles for data assimilation workflows. By combining geo-statistics within an uncertainty reduction scheme, the method addresses key modeling gaps encountered when modelling a Brazilian Pre-Salt carbonate.
Tag: fluid dynamics
Optimizing Gas Export Flexibility for Complex Offshore Reservoirs: A Brazilian Case
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
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
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.
Numerical Simulation Study of Relative Permeability Hysteresis in a Fractured Carbonate Reservoir Subjected to Water-Alternating-Gas Injection (WAG-CO2)
Integrated Multi-Scale Pore Characterization of Carbonate Rocks in the Barra Velha Formation, Santos Basin, Brazil
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.