The major oil fields are currently in the middle and late stages of waterflooding. The water channels between the wells are serious, and the injected water does little effect. The importance of profile control and water blocking has been identified. In this paper, the decision-making technique for water shutoff is investigated by the fuzzy evaluation method, FEM, which is improved using a random forest, RF, classification model. A machine learning random forest algorithm was developed to identify candidate wells and to predict the well performance for water shutoff operation. A data set consisting of 21 production wells with three-year production history is used, where out of the mentioned well data, 70% of them are implemented for training and the remaining are used for testing the model. After fitting the model, the new weights for the factors are established and decision-making is made. Accordingly, 16 wells out of 21 wells are selected by the FEM where 8 wells out of 21 wells are selected by the new factor weight created by RF for water shutoff. A numerical simulation model is established to plug the selected wells by both methods after which the influence of plugging on water cut, daily oil production, and cumulative oil production is compared. The paper shows that the reservoir had a better performance after eight wells were selected using a new weighting system created by RF instead of the 16 wells that were selected using the FEM model. The paper also states that the new weighting model’s accuracy improved the decision-making abilities of the wells.
Most of the oilfields are currently experiencing intermediate to late stages of oil recovery by waterflooding. Channels were created between the wells by water injection and its effect on the oil recovery is less. The use of water plugging profile control is required to control excessive water production from an oil reservoir. First, the well selection for profile control using the fuzzy evaluation method (FEM) and improvement by random forest (RF) classification model is investigated. To identify wells for profile control operation, a fuzzy model with four factors is established; then, a machine learning RF algorithm was applied to create the factor weight with high accuracy decision-making. The data source consists of 18 injection wells, with 70% of the well data being utilized for training and 30% for model testing. Following the fitting of the model, the new factor weight is determined and decisions are made. As a consequence, FEM selects 7 out of 18 wells for profile control, and by using the factor weight developed by RF, 4 out of 18 wells are chosen. Then, the profile control is conducted through a foam system proposed by laboratory experiments. A computer molding group numerical simulation model is created to profile the wells being selected by both methods, FEM and RF. The impact of foam system plugging on daily oil production, water cut, and cumulative oil production of both methods are contrasted. According to the study, the reservoir performed better when four wells were chosen by the weighting system developed by RF as opposed to seven wells that were chosen using the FEM model during the effective period. The weighting model developed by RF accurately increased the profile control wells' decision-making skills.
The purpose of the paper is to use the BuckleyLeverett frontal displacement theory to evaluate the recovery of oil by waterflooding for the Angut oilfield in northern Afghanistan. The waterflooding technique, in which oil is displaced by injecting water into the underlying petroleum reservoir, is investigated for the reservoir field where oil extraction has not been started. The effectiveness of the waterflooding technique is demonstrated by laboratory experiments using a horizontal plane model. The relative permeabilities of oil and water, residual oil saturation and irreducible water saturation are inspected through the experiments. The theory is then applied to the Angut oilfield to evaluate the amount of oil recovery from Angut oilfield by waterflooding technique.
Hydrocarbons represent an important natural resource for the rehabilitation and sustainable development of Afghanistan. In this paper, the use of waterflooding is demonstrated for the petroleum reservoirs of the Kashkari oilfield in northern Afghanistan. The technique stands on the Buckley-Leverett frontal-displacement theory, which enables computation of the progress of the waterfront in the reservoir. The oil and water relative permeabilities, the irreducible water saturation, and the residual oil saturation are obtained from a laboratory experiment. The technique is conducted to the Kashkari oilfield to predict the feasible quantity of the oil that could be produced from this reservoir. As a result, the Buckley-Leverett waterflooding technique recovered 67 MMBBL oil of Kashkari oilfield in 6200 days.
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