Optimization and Performance Evaluation of a Foam Plugging Profile Control Well Selection System.

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.

Using starch graft copolymer gel to assist the CO2 huff-n-puff process for enhanced oil recovery in a water channeling reservoir.

The CO2 huff-n-puff process is an effective method to enhance oil recovery (EOR) and reduce CO2 emissions. However, its utilization is limited in a channeling reservoir due to early water and gas breakthrough. A novel starch graft copolymer (SGC) gel is proposed for treating the channels and assisting with the CO2 huff-n-puff process. Firstly, the bulk and dynamic performances of the SGC gel including rheology, injectivity and plugging ability are compared with the polymer gel in the laboratory. Then, 3D physical models with water channels are established to reveal the EOR mechanisms of gel assisted CO2 huff-n-puff. Several pilot tests of gel assisted CO2 huff-n-puff are also discussed in this paper. The bulk and dynamic experimental results show that although these two gelants have similar viscosities, the SGC gelant has a better injectivity compared with the polymer gelant. The SGC gel is predominantly a viscous solution, which make it easier to flow through the pore throats. The RF of the SGC gelant is only 0.58 times that of the polymer gelant. After the gelation, a 3D network-like gel with a viscosity of 174 267 mPa s can be formed using the SGC gelant. The RRF of the SGC gel is about three times that of the polymer gel, which shows that the SGC gel has a stronger plugging ability within the porous media. The 3D experimental results show that four cycles of gel assisted CO2 huff-n-puff can achieve an EOR of 11.36%, which is 2.56 times that of the pure CO2 huff-n-puff. After the channels are plugged by the SGC gel, the remaining oil of the near-wellbore area can be first extracted by CO2, and the oil of the deep formation can then be effectively displaced by the edge water. Pilot tests on five wells were conducted in the Jidong Oilfield, China, and a total oil production of 3790.86 m3 was obtained between 2016 and 2021. The proposed novel SGC gel is suitable for assisting with the CO2 huff-n-puff process, which is a beneficial method for further EOR in a water channeling reservoir.

Decision-Making Techniques for Water Shutoff Using Random Forests and Its Application in High Water Cut Reservoirs.

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.

N2-foam-assisted CO2 huff-n-puff process for enhanced oil recovery in a heterogeneous edge-water reservoir: experiments and pilot tests.

The CO2 huff-n-puff process is an effective method to enhance oil recovery; however, its utilization is limited in heterogenous edge-water reservoirs due to the severe water channeling. Accordingly, herein, a stable N2 foam is proposed to assist CO2 huff-n-puff process for enhanced oil recovery. Sodium dodecyl sulfate (SDS) and polyacrylamide (HPAM) were used as the surfactant and stabilizer, respectively, and 0.3 wt% of SDS + 0.3 wt% of HPAM were screened in the laboratory to generate a foam with good foamability and long foam stability. Subsequently, dynamic foam tests using 1D sand packs were conducted at 65 °C and 15 MPa, and a gas/liquid ratio (GLR) of 1 : 1 was optimized to form a strong barrier in high permeable porous media to treat water and gas channeling. 3D heterogeneous models were established in the laboratory, and N2-foam-assisted CO2 huff-n-puff experiments were conducted after edge-water driving. The results showed that an oil recovery of 13.69% was obtained with four cycles of N2-foam-assisted CO2 injection, which is twice that obtained by the CO2 huff-n-puff process. The stable N2 foam could temporarily delay the water and gas channeling, and subsequently, CO2 fully extracted the remaining oil in the low permeable zones around the production well. Pilot tests were conducted in 8 horizontal wells, and a total oil production of 1784 tons with a net price value (NPV) of $240 416.26 was obtained using the N2-foam-assisted CO2 huff-n-puff process, which is a profitable method for enhanced oil recovery in heterogenous reservoirs with edge water.