The injection of CO2 has been in global use for enhanced oil recovery (EOR) as it can improve oil production in mature fields. It also has environmental benefits for reducing greenhouse carbon by permanently sequestrating CO2 (carbon capture and storage (CCS)) in reservoirs. As a part of numerical studies, this work proposed a novel application of an artificial neural network (ANN) to forecast the performance of a water-alternating-CO2 process and effectively manage the injected CO2 in a combined CCS–EOR project. Three targets including oil recovery, net CO2 storage, and cumulative gaseous CO2 production were quantitatively simulated by three separate ANN models for a series of injection frames of 5, 15, 25, and 35 cycles. The concurrent estimations of a sequence of outputs have shown a relevant application in scheduling the injection process based on the progressive profile of the targets. For a specific surface design, an increment of 5.8% oil recovery and 4% net CO2 storage was achieved from 25 cycles to 35 cycles, suggesting ending the injection at 25 cycles. Using the models, distinct optimizations were also computed for oil recovery and net CO2 sequestration in various reservoir conditions. The results expressed a maximum oil recovery from 22% to 30% oil in place (OIP) and around 21,000–29,000 tons of CO2 trapped underground after 35 cycles if the injection began at 60% water saturation. The new approach presented in this study of applying an ANN is obviously effective in forecasting and managing the entire CO2 injection process instead of a single output as presented in previous studies.
Heavy-oil resources represent a large percentage of global oil and gas reserves, however, owing to the high viscosity, enhanced oil recovery (EOR) techniques are critical issues for extracting this type of crude oil from the reservoir. According to the survey data in Oil & Gas Journal, thermal methods are the most widely utilized in EOR projects in heavy oil fields in the US and Canada, and there are not many successful chemical flooding projects for heavy oil reported elsewhere in the world. However, thermal methods such as steam injection might be restricted in cases of thin formations, overlying permafrost, or reservoir depths over 4500 ft, for which chemical flooding becomes a better option for recovering crude oil. Moreover, owing to the considerable fluctuations in the oil price, chemical injection plans should be employed consistently in terms of either technical or economic viewpoints. The numerical studies in this work aim to clarify the predominant chemical injection schemes among the various combinations of chemical agents involving alkali (A), surfactant (S) and polymer (P) for specific heavy-oil reservoir conditions. The feasibilities of all potential injection sequences are evaluated in the pre-evaluation stage in order to select the most efficient injection scheme according to the variation in the oil price which is based on practical market values. Finally, optimization procedures in the post-evaluation stage are carried out for the most economic injection plan by an effective mathematic tool with the purpose of gaining highest Net Present Value (NPV) of the project. In technical terms, the numerical studies confirm the predominant performances of sequences in which alkali-surfactant-polymer (ASP) solution is injected after the first preflushing water whereby the recovery factor can be higher than 47%. In particular, the oil production performances are improved by injecting a buffering viscous fluid right after the first chemical slug rather than using a water slug in between. The results of the pre-evaluation show that two sequences of the ASP group have the highest NPV corresponding to the dissimilar applied oil prices. In the post-evaluation, the successful use of response surface methodology (RSM) in the estimation and optimization procedures with coefficients of determination R 2 greater than 0.97 shows that the project can possibly gain 4.47 $MM at a mean oil price of 46.5 $/bbl with the field scale of a quarter five-spot pattern. Further, with the novel assumption of normal distribution for the oil price variation, the chemical flooding sequence of concurrent alkali-surfactant-polymer injection with a buffering polymer solution is evaluated as the most feasible scheme owing to the achievement of the highest NPV at the highly possible oil price of 40-55 $/bbl compared to the other scheme.
This study presents a new application of coal-derived graphene quantum dots (GQD) in stabilizing surfactant-based foams. The methane foam generated by the surfactant itself is susceptible to rapid collapse due to various factors. When the GQDs are added to the surfactant in a mass ratio between 1:8 to 1:16, they self-assemble at the lamella and prevent liquid drainage and coalescence. The nanofluid composed of GQD and amphoteric surfactant reduced both the oil–brine and brine–gas interfacial tension to a greater extent compared to the surfactant alone. In addition, GQD helped alter the rock wettability to strongly water-wet conditions, as compared to weakly water-wet conditions with pure surfactant. The foam formed by the nanofluid was made up of smaller uniformly shaped bubbles with a thick lamella, whereas the foam formed by the surfactant had large polyhedral shaped bubbles with a thin lamella. The transmission electron microscope micrographs of the nanofluid emulsion with crude oil showed that the GQDs are highly interfacially active and tend to assemble on the surface of the oil droplets. Next, the foam stability and strength were investigated with pure surfactant and nanofluid in high salinity brine using water- and oil-wet sandpacks at reservoir conditions relevant to the Bakken formation. The dependence of foam half-life and steady-state apparent viscosity was studied as a function of surfactant and GQD concentration, gas fraction, flow rate, and brine salinity. It was observed that the addition of GQD increases both the foam half-life and steady-state apparent viscosity compared to pure surfactant. This work paved the way for the application of novel carbonaceous nanoparticles under challenging conditions where traditional nanoparticles cannot be used.
Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30% corresponding to the number of samples used for training-validation-testing was selected for estimation with the total coefficient of determination of 0.986 and a root mean square error of 1.63%. In terms of model application, the chemical concentration and injection strategy were optimized to maximize the net present value (NPV) of the project at a specific oil price from the just created ANN model. To evaluate the feasibility of the project comprehensively in terms of market variations, a range of oil prices from 30 $/bbl to 60 $/bbl referenced from a real market situation was considered in conjunction with its probability following a statistical distribution on the NPV computation. Feasibility analysis of the optimal chemical injection scheme revealed a variation of profit from 0.42 $MM to 1.0 $MM, corresponding to the changes in oil price. In particular, at the highest possible oil prices, the project can earn approximately 0.61 $MM to 0.87 $MM for a quarter five-spot scale. Basically, the ANN model generated by this work can be flexibly applied in different economic conditions and extended to a larger reservoir scale for similar chemical flooding projects that demand a quick prediction rather than a simulation process.
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