Hydraulic fracturing operations affect reservoir flow dynamics and increase production in unconventional tight reservoirs. The control of fracture growth and geometry presents challenges in formations in which the boundary lithologies are not highly stressed in comparison to the pay zone, thus failing to prevent the upward migration of fractures. Several factors influence the growth and geometry of fractures, including reservoir, wellbore, and fluid/proppant parameters. Successful results require a thorough knowledge of reservoir parameters, including stress distribution and the appropriate use of corresponding wellbore components and fluid/proppant. The success of a hydraulic fracturing treatment is highly correlated with control of the created fracture geometry.This paper discusses a study in which a numerical fracture model is used to design the fractures in a tight oil reservoir. Fracture treatment designs include the selection of fracturing fluids, additives, proppant materials, injection rate, pumping schedule, and fracture dimensions. Using the fracture model, a statistically representative synthetic set of data is generated for each parameter to build data-driven models.The performance of the data-driven models is validated by comparing the results to a numerical model, and considering the significance of parameters, including size, number, location, phasing angle of perforations, fluid and proppant type, rock strength, porosity, and permeability on the fracture design optimization using various fracture models. Data-driven predictive models are generated by using neural networks (NN) and support vector machine (SVM) algorithms. Optimum values of model parameters are also investigated.The SVM and NN models are used to optimize the fracturing treatments per well, and are evaluated based on accuracy and computational complexity. Based on the performances of the models, model parameters are adjusted to obtain fit-for-purpose well-based hydraulic fracturing models.
Economic production from tight-oil formations requires the same hydraulic fracturing techniques used during production of shale gas, and the same horizontal well technology is often used. Tight-oil formations are heterogeneous and greatly vary over relatively short distances. Thus, even in a single horizontal drilled hole, the amount recovered can vary, just as recovery within a field or even between adjacent wells can vary. This can make evaluation and decisions regarding profitability difficult. In relatively thinner pay zones, horizontal or slanted wells can be successful in terms of productivity as long as robust reservoir management is applied with well designs that consider both geology and operational control variables. Thus, it is crucial to understand every control and uncertainty parameter to help maximize efficiency and recovery within such systems.Robust commercial optimization and uncertainty software is coupled with a full-physics commercial simulator that models this phenomenon to investigate the significance of major parameters on the performance of horizontal wells in tight-oil formations.Slanted wells provide more flexibility and access to pays than vertical wells drilled from the same surface location, which is of more significance in tight formations because of less communication between zones. The results of the study not only confirm this, but also show the increased value provided using slanted wells compared to vertical wells in tight formations. The study also illustrates the significance of each optimization and uncertainty variable in terms of the success of recovery from slanted horizontal wells in tight formations.The results and sensitivities are compared and discussed considering a comprehensive literature review of recycling gas-condensate reservoirs using different process optimization methods. The significance of all major parameters is outlined using tornado charts to serve as a practical example for optimization of similar future applications. Vertical, Slanted and Horizontal WellsThe technology for drilling horizontal wells to increase reservoir drainage areas has been used for many years. As directional drilling technology has advanced, the cost of drilling directional wells has been reduced, and the accuracy of drilling has been significantly improved.
Water alternating gas (WAG) injection has been widely used for the last 50 years throughout the world. The typical improved oil recovery (IOR) potential for WAG injection compared with water injection is 5 to 10%. It was originally intended to improve sweep efficiency during gas flooding, with intermittent slugs of water and gas designed to follow the same route through the reservoir. Mechanisms in WAG injection include microscopic effects, particularly in cases where three-phase flow and hysteresis are important for the IOR effect. Injection of gas usually aids an ongoing waterflood, and finding technical and commercial methods to reduce gas costs would be useful. Water injection alone tends to sweep the lower parts of a reservoir, while gas injected alone sweeps more of the upper parts of a reservoir because of gravitational forces. Gas represents a large fraction of the total cost, making WAG injection an expensive method. Thus, optimizing WAG injection is not only crucial in terms of recovery but also economics, especially where gas is expensive and/or limited. In this study, the significance of key components in a WAG injection process on SPE's 5th Comparative Solution Project (CSP) is presented that models the WAG process through a pseudo-miscible formulation by means of coupling a full-physics reservoir simulator with commercial optimization and uncertainty software. The results are analyzed and presented in a comparative manner by means of tornado charts showing the significance of each decision and uncertainty variable.
With recent advances in technology and because of its application in the oil industry, real-time production optimization in intelligent fields has become more practical. The optimal control methods developed previously are now applied in smart workflows to maximize recovery or net present value (NPV). Development of new and mature fields can be improved with integrated workflows that account for the technical and economic aspects of hydrocarbon recovery, as well as the potential impact of uncertainty. This approach is important for a high-impact decision-making process such as well placement. Optimization of well placement in complex reservoirs requires a thorough understanding of both local and global optimization methods, key parameters, and constraints. A major drawback in this process is that most optimization algorithms are incorrectly understood and applied by the engineers. In this paper, that gap is addressed by presenting not only the theoretical background but also the application of optimization algorithms existing in workflows that couple reservoir simulation with commercial optimization and uncertainty software updated with continuous data flow from the field. Different optimization techniques are used for optimal well placement. Some of the most commonly used techniques are exploratory, direct, and gradient optimization methods. This paper provides a thorough analysis of these approaches and illustrates the performance evaluations using reservoir simulation case studies as applied in intelligent fields. The analysis considers the computational complexity, scale of the problem, and definition of the objective function. The performance evaluation builds on the analysis to compare the optimal well location, time of convergence to optimal solution, and the corresponding reservoir simulation results. The intended application of this study is to assist in selecting the right approach to make sound engineering decisions for substantial improvement in field production rather than selection of methods which may lead to decreased ultimate recovery.
Throughout the previous few years, substantial attention has been paid to the usage of smart well technologies to help improve recovery, particularly with technological improvements and an increasing expanse of opportunities in more challenging and rewarding assets. The fundamental focus has been to propose and develop workflows that integrate several surface/subsurface subprocesses and automate the entire workflow. In cases where significant investment is made to complete smart wells with remotely controlled inflow control valves (ICV), reservoir sweep becomes decisive when evaluating the efficient recovery. Application of this technology has been challenging because it is a modern concept. This study showcases the effective application of ICVs within intelligently completed fields to satisfy the objective function by augmenting reservoir sweep and oil recovery. In this study, a commercial full-physics numerical reservoir simulator has been used to evaluate a synthetic simulation model mimicking a realistic reservoir with waterflood. The wells are installed with smart well completions using ICVs that are controlled by conditional statements called procedures. The decision parameters varied to determine if the level of ICV opening within producer wells is water-cut and well-injection rates. Then, the cumulative oil recovery is used as an objective function to increase the maximum oil recovery. The ultimate goal is to reach the highest net present value (NPV) through having higher cumulative oil production values with the lower water injection and water production rates. The relatively high expenditure linked with installing intelligent completions within wells drive further the importance to apply and study the advantages of this technology on multiple, diverse cases coupled with specifically planned workflows. Recent studies have shown that a robust reservoir management plan along with an effective application of ICVs within intelligently completed fields can augment reservoir sweep and oil recovery. The results of the study demonstrated the positive impact when using ICVs on NPVs calculated compared to the base case where traditional completions have been used. It is also shown that, without a robust reservoir management plan, the use of intelligent completions might not always be successful. Augmenting the performance of the reservoirs, in addition to looking at the individual well performance, forms the crux of a sound reservoir management plan. This study, therefore, examines the big picture by following a field-wide approach rather than focusing solely on individual or near-well performance. The core of this study is to provide a framework of effective integration of data from leading performance indicators attributing to intelligent well completions, with the ultimate goal of optimizing the reservoir recovery.
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