Experimental investigation of surfactant adsorption during EOR and soil pollution control processes:Influence of surfactant concentration,water salinity,and clay type

Surfactant injection is a well-established method of chemical EOR processes.Surfactant adsorption into clay layers can prevent their proper performance and thus reduce the oil recovery factor.On the other hand,this adsorption property of clay materials can be used to prevent surface and underground water pollution and reduce soil pollution.In this experimental study,the effect of surfactant concentration,electrolyte type(NaCl and MgCl_(2)),and the solution salinity on fluid adsorption into the interlayer space of different clay types(bentonite and kaolinite)was investigated.XRF analysis was conducted on two relevant clay samples,and immersion and Washburn tests were performed on the desired samples with the Sigma 700 setup.Then,according to the clay type,the most optimal conditions were introduced for the surfactant solution used in the two areas of EOR and environmental processes related to reducing soil pollution.In the EOR processes,the optimal condition for the lowest adsorption amount is C(with 1 CMC concentration and salinity of 100,000 ppm for NaCl salt).This fluid works better in kaolinite formations.In the environmental field related to the reduction of soil pollution,if the pollutants we are looking for are R and S(with alkyl benzene sulfonic acid as the dominant agent),bentonite has a better performance than kaolinite in terms of adsorption and subsequently pollution control.If the polluting fluid contains MgCl_(2) ions in the exact salinity values,the adsorption amount and soil pollution control will be higher for both adsorbent clays than if our fluid has NaCl salinity.The study's findings have a wide range of applications in surfactant flooding designs,surfactant adsorption optimization,and can be generalized to other detergent types.

Effect of alumina and silica nanocomposite based on polyacrylamide on light and heavy oil recovery in presence of formation water using micromodel

Increasing world request for energy has made oil extraction from reservoirs more desirable.Many novel EOR methods have been proposed and utilized for this purpose.Using nanocomposites in chemical flooding is one of these novel methods.In this study,we investigated the impact of six injection solutions on the recovery of light and heavy oil with the presence of two different brines as formation water using a homogenous glass micromodel.All of the injection solutions were based on a 40,000 ppm Na Cl synthetic seawater(SSW),one of which was additive free and the others were prepared by dispersing nanocomposite silica-based polyacrylamide(NCSP),nanocomposite alumina-based polyacrylamide(NCAP),the combination of both nanocomposites silica and alumina based on polyacrylamide(NCSAP),surfactant(CTAB)and polyacrylamide(PAM)with a concentration of 1000 ppm as additives.The Stability of nanocomposites was tested against the salinity of the brine and temperature using salinity and DSC tests which were successful.Alongside stability tests,IFT,contact angle and oil recovery measurements were made.Visual results revealed that in addition to the effect of silica and alumina nanocomposite in reducing interfacial tension and wettability alteration,control of mobility ratio caused a major improvement in sweeping efficiency and oil recovery.According to the sweeping behavior of injected fluids,it was found that the main effect of surfactant was wettability alteration,for polyacrylamide was mobility control and for nanocomposites was the reduction of interfacial tension between oil and injected fluid,which was completely analyzed and checked out.Also,NCSAP with 95.83%and 70.33%and CTAB with 84.35%and 91%have the highest light oil recoveries at 250,000 ppm and 180,000 ppm salinity,respectively which is related to the superposition effect of interactions between nanocomposites,solution and oil.Based on our results it can be concluded that the most effective mechanism in oil recovery was IFT reduction which was done by CTAB reduction also by using a polymer-based nanocomposite such as NCSAP and adding the mobility control factor,the oil recovery can be further enhanced.In the case of heavy oil recovery,it can be concluded that the mobility control played a much more effective role when the PAM performed almost similarly to the CTAB and other nanocomposites with a recovery factor of around 17%.In this study,we tried to investigate the effect of different injection solutions and their related mechanisms on oil recovery.

An application of a genetic algorithm in co-optimization of geological CO_(2) storage based on artificial neural networks

Global warming,driven by human-induced disruptions to the natural carbon dioxide(CO_(2))cycle,is a pressing concern.To mitigate this,carbon capture and storage has emerged as a key strategy that enables the continued use of fossil fuels while transitioning to cleaner energy sources.Deep saline aquifers are of particular interest due to their substantial CO_(2) storage potential,often located near fossil fuel reservoirs.In this study,a deep saline aquifer model with a saline water production well was constructed to develop the optimization workflow.Due to the time-consuming nature of each realization of the numerical simulation,we introduce a sur-rogate aquifer model derived from extracted data.The novelty of our work lies in the pioneering of simultaneous optimization using machine learning within an integrated framework.Unlike previous studies,which typically focused on single-parameter optimiza-tion,our research addresses this gap by performing multi-objective optimization for CO_(2) storage and breakthrough time in deep sa-line aquifers using a data-driven model.Our methodology encompasses preprocessing and feature selection,identifying eight pivotal parameters.Evaluation metrics include root mean square error(RMSE),mean absolute percentage error(MAPE)and R^(2).In predicting CO_(2) storage values,RMSE,MAPE and R^(2)in test data were 2.07%,1.52% and 0.99,respectively,while in blind data,they were 2.5%,2.05% and 0.99.For the CO_(2) breakthrough time,RMSE,MAPE and R^(2) in the test data were 2.1%,1.77% and 0.93,while in the blind data they were 2.8%,2.23% and 0.92,respectively.In addressing the substantial computational demands and time-consuming nature of coup-ling a numerical simulator with an optimization algorithm,we have adopted a strategy in which the trained artificial neural network is seamlessly integrated with a multi-objective genetic algorithm.Within this framework,we conducted 5000 comprehensive experi-ments to rigorously validate the development of the Pareto front,highlighting the depth of our computational approach.The findings of the study promise insights into the interplay between CO_(2) breakthrough time and storage in aquifer-based carbon capture and storage processes within an integrated framework based on data-driven coupled multi-objective optimization.

Data driven prediction of oil reservoir fluid properties

Accuracy of the fluid property data plays an absolutely pivotal role in the reservoir computational processes.Reliable data can be obtained through various experimental methods,but these methods are very expensive and time consuming.Alternative methods are numerical models.These methods used measured experimental data to develop a representative model for predicting desired parameters.In this study,to predict saturation pressure,oil formation volume factor,and solution gas oil ratio,several Artificial Intelligent(AI)models were developed.582 reported data sets were used as data bank that covers a wide range of fluid properties.Accuracy and reliability of the model was examined by some statistical parameters such as correlation coefficient(R2),average absolute relative deviation(AARD),and root mean square error(RMSE).The results illustrated good accordance between predicted data and target values.The model was also compared with previous works and developed empirical correlations which indicated that it is more reliable than all compared models and correlations.At the end,relevancy factor was calculated for each input parameters to illustrate the impact of different parameters on the predicted values.Relevancy factor showed that in these models,solution gas oil ratio has greatest impact on both saturation pressure and oil formation volume factor.In the other hand,saturation pressure has greatest effect on solution gas oil ratio.

Mechanistic simulation of fracture effects on miscible CO_(2)injection

Miscible injection of carbon dioxide(CO_(2))into oil reservoirs as an Enhanced Oil Recovery(EOR)method has proved to be highly advantageous.According to the volume of the world's recoverable oil resides inside the fractured reservoirs,investigation of the controlling parameters in the efficient injection of miscible CO_(2) is of paramount importance,mainly owing to the intricacies and complexities associated with this process.This complexity in fractured reservoirs arises due to the presence of two distinct media for fluid transfer(i.e.,matrix and fracture network)and the corresponding differences in fluid velocities.Accordingly,performance of miscible carbon dioxide injection in these reservoirs was investigated through mechanistic simulation model in the form of dual-porosity(DP),and dual porosity-dual permeability(DPP).Moreover,due to limited supply and high injection costs of this gas in its pure form,performance of the miscible CO_(2) injection combined with C_(1),N_(2),and H_(2)S was also surveyed and compared to pure gas injection case.A sensitivity analysis was also performed based on fracture porosity,fracture horizontal and vertical permeability,matrix horizontal permeability,block height shape factor,matrix capillary pressure,and impure injected components in DP and DPP models,showing that matrix horizontal permeability and capillary pressure have the greatest,and porosity has the lowest impact on miscibility performance and oil recovery in these models.In the end,after investigating the effect of different injection cases on miscibility performance and oil recovery,it was concluded that the highest oil recovery in miscible gas injection obtained through optimization of a gas composition having the lowest minimum miscibility pressure(MMP)and the lowest density.