Petroleum engineers are always in a race to maximize the recovery factor out of naturally trapped hydrocarbon resources. Unconventional resources such as organic-rich shales have unlocked significant reserves attributed to the novel production technologies of lateral drilling assisted by hydraulic fracturing. Even though such techniques have enabled the exploitation of shales, the ultimate recovery remained fractional, a challenge to be answered through further improvement. Carbon dioxide injection in unconventional resources, which was initially implemented for coalbed methane, has been recently an active area of investigation for organic-rich shales. In this paper, we present a molecular modeling study of carbon dioxide injection in the organic matter of the shale matrix. We built the molecular model, consistent with the repeated organic matter characterization in the literature. Molecular dynamics (MD) protocol was developed to form a three-dimensional (3-D) configuration of kerogen, followed by Gibbs Monte Carlo simulation for the adsorption/desorption calculations, and self-diffusivity calculations through MD. The aim was to delineate the impact of carbon dioxide injection on the adsorption/desorption behavior coupled with its influence on the transport. Injection of carbon dioxide was found to shift the adsorption isotherm favoring the depletion of methane. The ultimate recovery raised from 54% (no injection of CO 2 ) up to 92% depending on the carbon dioxide concentration and its temperature. Moreover, the injection of carbon dioxide was found to have a minimal impact on the self-diffusivity of methane in kerogen bodies and their associated microcracks.

Carbon dioxide has gradually found widespread usage in the field of science and engineering while various efforts have focused on ways to combat the menace resulting from the release of this compound in the atmosphere. A major approach to combating this release is by storage in various geological formations ranging from depleted reservoir types such as saline aquifers to other carbon sinks. In this research study, we reviewed the experimental, modeling, and field studies related to the underground storage of CO2. A considerable amount of research has been conducted in simulating and modeling CO2 sequestration in the subsurface. This review highlights some of the latest contributions. Additionally, the impact of CO2 sequestration on its surroundings due to chemical reactions, adsorption, capillarity, hysteresis, and wettability were reviewed. Some major challenges associated with CO2 injection have also been highlighted. Finally, this work presents a brief history of selected field scale projects such as Sleipner, Weyburn, In Salah, Otway Basin, Snøhvit, Alberta, Boundary Dam, Cranfield, and Ketzin. Thus, this study provides a guide of the CO2 storage process from the perspectives of experimental, modelling, and existing field studies.

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Efficient recovery of hydrocarbon resources is one of the challenges facing the oil and gas industry. In an enhanced oil recovery (EOR) scheme, optimal placement of wells plays an important role in determining the amount of oil that can be recovered with the selected EOR process. Once the reservoir flow paths have been established, it necessary to find an optimal configuration of wells that will yield the highest net present value (NPV). Traditionally, well placement optimization (WPO) has been done through experience and use of quality maps. However, in recent times, there has been a gradual shift from traditional methods to automatic well placement that uses gradient-based or stochastic search algorithms to locate the optimal positions of wells. This technology has enabled improvement in the decision making process. Despite the successes achieved, the optimization tools rarely enforce well spacing constraints during the optimization process. This often results in well configurations with high NPV but also with physically unrealistic well positions. In this paper, we propose to solve the well placement optimization problem constrained to any desired minimum well spacing. Minimum well spacing here refers to the minimum distance between any two wells that a company or an asset team considers technically safe. First, we develop the nonlinear inequality constraints needed to enforce minimum well spacing, then formulate the well placement problem as a constrained optimization problem and subsequently adopt the penalty approach to solve the constrained well placement problem. The covariance-matrix adaptation evolutionary strategy (CMA-ES) was used as the global optimizer in solving the optimization problem. Two examples were used to show the effectiveness of the approach. Results obtained from this approach were compared with those obtained from the unconstrained WPO. The results show that the method can successfully determine optimal well locations without violating any of the constraints. In contrast, the unconstrained optimization approach failed to satisfy some of the nonlinear constraints and in some cases yielded well configurations in which two or more wells are placed at exactly the same location (on top of each other).

As the majority of conventional reservoirs are reaching maturity, the attention is gradually shifting towards unconventional and heavy oil reservoirs. Steam flooding, Miscible/Immiscible CO2 flooding, Polymer flooding and Water Alternating Gas (WAG) are the most common EOR techniques currently being employed in the industry. However, it has been recently proved that Polymer alternating gas (PAG) is a better alternative to the other EOR processes as it provides improved sweep efficiency (Zhang et. al. 2010, Li and Schecter, 2014, Kong et. al., 2015). A PAG process alternately inject miscible CO2 and water mixed with polymer. Different parameters directly affect the performance of this method. Thus, it is important that these parameters are carefully selected to increase the recovery along with the profitability. In this paper, an attempt has been made to optimize a PAG process for five production and five injection wells using two different global optimization algorithms. One injection cycle of a PAG process constitutes of two stages – (i) Injection of miscible CO2 (ii) Injection of polymer. CO2 dissolves in oil, swelling it and decreasing the viscosity thereby increasing the mobility of oil. The injection of polymer increases the sweep efficiency and reduces viscous fingering. In this paper the operational parameters that have been selected for optimization are well locations, number of injection cycles required, production BHP, CO2 injection rate, CO2 injection time, polymer injection rate and polymer injection time and the concentration of the polymer that needs to be injected. The algorithms used are the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the Particle Swarm Optimization (PSO). A synthetic reservoir model was used to study the performance of the method. The Net Present Value (NPV) was used as the objective function to gauge the suitability of the solution and hence optimize the different parameters to obtain the highest NPV. Both used optimization algorithms yielded NPV that were well within the acceptable range. Additionally, sensitivity studies were conducted which showed that PAG gave a higher recovery efficiency when compared to the other EOR processes. Although some case studies have been performed to show the performance of the PAG process, to the best of our knowledge, no studies have been conducted on the use of global optimization algorithms for the estimation of the operational parameters of the PAG process.

Adsorption is a rock surface phenomenon and has increasingly become popular, especially in particle-transport applications across many fields. This has drawn a remarkable number of publications from the industry and academia in the last decade, with many review articles focused on adsorption of polymers, surfactants, gas, and nanoparticles in porous media with main applications in Enhanced Oil Recovery (EOR). The discussions involved both experimental and modeling approaches to understanding and efficiently mimicking the particle transport in a bid to solve pertinent problems associated with particle retention on surfaces. The governing mechanisms of adsorption and desorption constitute an area under active research as many models have been proposed but the physics has not been fully honored. Thus, there is a need for continuous research effort in this field. Although adsorption/desorption process is a physical phenomenon and a reversible process resulting from inter-molecular and the intramolecular association between molecules and surfaces, modeling these phenomena requires molecular level understanding. For this reason, there is a wide acceptance of molecular simulation as a viable modeling tool among scientists in this area. This review focuses on existing knowledge of adsorption modeling as it relates to the petroleum industry cutting across flow through porous media and EOR mostly involving polymer and surfactant retention on reservoir rocks with the associated problems. The review also analyzes existing models to identify gaps in research and suggest some research directions to readers.

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Of concern, in the development of oil fields, is the problem of determining the optimal locations of wells and the optimal controls to place on the wells. Extraction of hydrocarbon resources from petroleum reservoirs in a cost effective manner requires that the producers and injectors be placed at optimal locations and optimal controls be imposed on the wells. While the optimization of well locations and well controls plays an important role in ensuring that the net present value of the project is maximized, optimization of other factors such as well type and number of wells also plays important roles in increasing the profitability of investments. Until very recently, improving the net worth of hydrocarbon assets has been focused primarily on optimizing the well locations or well controls, mostly manually. In recent times, automatic optimization using either gradientbased algorithms or stochastic (global) optimization algorithms has become increasingly popular.We present a two-part series illustrating how to effectively optimize well placement and rates without dramatically increasing the size of the optimization problem. In this first part of the work, we present two approaches to reduce the number of design variables in well rate optimization. Polynomial and trigonometry models are proposed to model the change of well rates with time and these models are parameterized with coefficients which can be determined from any optimization method. Thus, instead of using well rates as the control variables in the optimization process, we use the parameters of equations that are able to model the variation of well rates with time. Each model has a specific number of coefficients to model the change in rate over time and each well has a distinct value for each coefficient in the model. This means that the number of variables is fixed regardless of the number of years of operating the wells. The total number of optimization parameters is thus the product of the number wells and the number of coefficients in the model. In this way the method allows us to find the average daily or monthly or annual well rate for each well. This significantly reduces the number of variables required and the time required in running the optimization algorithm. The method also makes it possible to use optimization algorithms that would otherwise break down when the number of design variables is too large.In the second part, a joint optimization approach to estimating optimal well locations, well rates, well type and well number is proposed. Our approach uses a set of well coordinates and a set of well controls as the optimization parameters. The set of well controls, however, covers both the negative and positive parts of the real line. The search interval of the well controls is divided into three parts, one part denoting the region where the well is an injector, a second part denoting the region where there is no well, and a third part denoting the region where the well is a producer. By this, the optimization algorithm is able to match every...