Role of high-density brines in reservoir development stages:A review

High-density brines have been recognized beneficial for oilfield applications,with various key areas such as drilling,completion and formation evaluation.High-density brines can play a critical role in the development and production of oil and gas reservoirs during the primary,secondary,and tertiary recovery phases.High-density brines can enhance the mobility and recovery of the oil in the reservoir by controlling the density and viscosity.However,a less attention has been given to the application of high-density brine in the area of reservoir development.This review is shedding light on a concise overview of reservoir development stages in association with the recovery mechanisms.In addition,most possible applications of high-density fluids have also been reviewed in the field of the reservoir development.In summary,this review state that high-density brines can be used to stimulate reservoirs by hydraulic fracturing during the primary recovery phase.However,the risk of increased interfacial tension,which relies on the density difference of two fluids,can trap more residual oil relative to conventional water flooding.In addition,high-density brines are effective in decreasing the mobility ratio and facilitating favorable displacement during polymer flooding.However,they can be least effective in alkaline flooding due to the high IFT related to large density differences.Thus,it is suggested to consider the utilization of sustainable high-density brines by taking into account effective factors in petroleum engineering aspects such as stimulation,secondary recovery and polymer flooding.

Analyzing the effect of steam quality and injection temperature on the performance of steam flooding

The heavy oil reservoirs are currently mainly targeted by thermal enhanced oil recovery technologies,particularly,steam flooding.Steam flooding is carried out by introducing heat into the reservoir to unlock the recovery of heavy oil by reducing oil viscosity.Several investigations were carried out to improve oil recovery by steam flooding.Most recently,high steam flooding is reported as an effective approach to improve recovery in high pressure heavy oil reservoirs.The oil recovery from steam flooding is sub-stantially affected by the steam quality and injection temperature.In this study,an attempt was made to look into the integration of parameters,i.e.steam quality and injection temperature upon steam flooding on oil recovery by using a simulation approach via ECLIPSE.The results obtained indicated that high temperature along with the moderate value of steam quality gives the best result regarding oil recovery for steam flooding in an economical way.

Assessment of parameters effectiveness in the reserve estimation methods applicable to coal bed methane reservoirs

The reserve estimation of coal bed methane(CBM)reservoirs is ascertained through the analytical methods(volumetric method,material balance equation and decline curve analysis).However,the adoption of reserve estimation methods depends on exploration stage and availability of the required parameters.This study deals with the analytical assessment of parameters that participate in effecting the reserve estimation of CBM reservoirs through the analytical techniques.The accurate measurement challenges always exist for the parameters which participate in the reserve estimation of the conventional and unconventional reservoirs because of the inclusion of limitations while measurement.Therefore,the impact of that measurement challenge must be assessed.The study specifies the impact of parametric change on the reserve estimation of CBM reservoirs so that the degree of parametric effectiveness is analyzed.Uncertain values are adopted which are associated during the evaluation of input parameters for each method to determine the overall impact on potential of CBM reserves.Results reveal that change in specific parameters considering each method provide relatively more effect on estimation of reserves.Thus,the measurement of parameters must be done accurately for assessing reserves of CBM reservoirs based on available methods.

Applicability of deep neural networks for lithofacies classification from conventional well logs: An integrated approach

Parametric understanding for specifying formation characteristics can be perceived through conven-tional approaches.Significantly,attributes of reservoir lithology are practiced for hydrocarbon explora-tion.Well logging is conventional approach which is applicable to predict lithology efficiently as compared to geophysical modeling and petrophysical analysis due to cost effectiveness and suitable interpretation time.However,manual interpretation of lithology identification through well logging data requires domain expertise with an extended length of time for measurement.Therefore,in this study,Deep Neural Network(DNN)has been deployed to automate the lithology identification process from well logging data which would provide support by increasing time-effective for monitoring lithology.DNN model has been developed for predicting formation lithology leading to the optimization of the model through the thorough evaluation of the best parameters and hyperparameters including the number of neurons,number of layers,optimizer,learning rate,dropout values,and activation functions.Accuracy of the model is examined by utilizing different evaluation metrics through the division of the dataset into the subdomains of training,validation and testing.Additionally,an attempt is contributed to remove interception for formation lithology prediction while addressing the imbalanced nature of the associated dataset as well in the training process using class weight.It is assessed that accuracy is not a true and only reliable metric to evaluate the lithology classification model.The model with class weight recognizes all the classes but has low accuracy as well as a low F1-score while LSTM based model has high accuracyas well as a high F1-score.