Optimizing Field Development in Data-Starved Reservoirs: Mitigating Economic Risks Through Stochastic Production Profiling and Fully Implicit Static-Dynamic Simulation
Authors: Gharieb Amr , El Hamady Abdelrafae , Gad Ramy , Lairet Bernardo Bohorquez , Rivas-Rivas Samuel 
Published: October 2024

Abstract
This paper introduces a comprehensive strategy for the evaluation and optimization of oil and gas reservoirs, particularly those with limited or no geological data. The methodology focuses on the integration of both surface and subsurface data, aiming to develop more accurate and efficient exploitation strategies even in environments characterized by high levels of uncertainty. The proposed approach builds on the foundations laid by Christie et al. (2013), who applied multi-objective algorithms to history matching in real fields but extended their work by addressing some of the inherent limitations in traditional reservoir modeling practices.
Typically, geologists provide three models - P10, P50, and P90 - to initiate reservoir simulations, which are designed to address structural uncertainties through volumetric calculations. However, these traditional approaches often fall short because they do not fully account for dynamic factors, such as saturation distribution driven by capillary effects and the transition zone within the reservoir. As a result, these models can produce overly optimistic estimates of the fluid in place, which can lead to inaccuracies in the reservoir management and development process.
To overcome these limitations, this study started with conducting 500 geological and dynamic simulations that incorporate a wide array of both static and dynamic elements. These elements include fault positions, target horizon geometries, porosity and permeability distributions, and critical fluid properties like formation volume factor, viscosity, and oil-water contact. The objective of these simulations was to evaluate the production profile of a single well over six months. This production profile was then integrated into the initial uncertainty assessment, which allowed for the selection of an ensemble of models that could accurately represent both the initial fluid in place and the production profile, along with its pressure response. Remarkably, this was achieved without the need to modify the properties of the original models provided by geologists to match historical production behavior.
Following the selection of the ensemble, which comprised 18 models, the study proceeded to analyze the forecasting and optimization capabilities of this ensemble. This was done using a deterministic field model that included a well-thought-out drilling strategy, consisting of 6 production wells and 5 injection wells. Once the responses from these models were evaluated, the operating conditions of the production wells were optimized with the goal of maximizing recovery from the reservoir. The extensive simulations and optimizations resulted in narrowing down the 500 initial realizations to 18 models that closely aligned with the Stock Tank Oil Initially in Place (STOIP) and the reservoir's pressure response. These models formed a robust ensemble that was instrumental in evaluating field development plans, providing a spectrum of potential solutions that could be tailored to the specific needs and conditions of the reservoir.
The paper extends beyond model selection and optimization by exploring the technical nuances of balancing history matching with simulator parallelism. It highlights that parallelism can significantly reduce computational time and power consumption, even when using personal machines rather than high-performance computing systems. This discussion highlights the importance of finding an optimal trade-off between computational efficiency and accuracy, which is crucial for making informed decisions in real-time reservoir management scenarios.
The integrated approach proposed in this study represents a substantial advancement in reservoir management, providing a robust and efficient framework for evaluating oil and gas reservoirs with limited data availability, particularly those with scarce geological data. By uniformly quantifying uncertainty and generating a range of potential solutions, this method not only optimizes field development plans but also enhances recovery factors, thereby maximizing the value extracted from the reservoir. This workflow sets a new benchmark for exploiting oil and gas fields under uncertain conditions, providing a more comprehensive approach to model creation that significantly improves the predictability and robustness of field development strategies. Ultimately, this approach unlocks the hidden potential within challenging environments, ensuring that the reservoir's resources are utilized to their fullest extent while minimizing risks and uncertainties.