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Better use of experience from other reservoirs for accurate production forecasting by learn-to-learn method

Petroleum Science 2024年第1期21卷 716-728页

作者： Hao-Chen Wang Kai Zhang Nancy Chen Wen-Sheng Zhou Chen Liu Ji-Fu Wang Li-Ming Zhang Zhi-Gang Yu Shi-Ti Cui Mei-Chun Yang China University of Petroleum(East China) 66 Changjiang West RoadQingdao West Coast New AreaQingdao266580ShandongChina University of Calgary 2500 University Dr NWCalgaryT2N 1N4AlbertaCanada Qingdao University of Technology Qingdao266071ShandongChina State Key Laboratory of Offshore Oil Exploitation Beijing100028China CNOOC Research Institute Ltd. Beijing100028China CNPC Tarim Oilfield Branch Company Korla841000XinjiangChina National Engineering Laboratory for Exploration and Development of Low-Permeability Oil and Gas Fields Xi'an710000ShaanxiChina Sinopec Matrix Co. LTDQingdao266000ShandongChina

To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods.

关键词： production forecasting Multiple patterns Few-shot learning Transfer learning

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Principal components methodology-A novel approach to forecasting production from liquid-rich shale(LRS)reservoirs

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Petroleum 2019年第3期5卷 227-242页

作者： Ibukun Makinde W.John Lee University of Houston Texas A&M UniversityUnited States

With increasing global demand for energy,the importance of unconventional shale oil and gas research cannot be over-emphasized.The oil and gas industry requires rapid and reliable means of forecasting production.Existing traditional decline curve analysis(DCA)methods have been limited in their ability to satisfactorily forecast production from unconventional liquid-rich shale(LRS)reservoirs.This is due to several causes ranging from the complicated production mechanisms to the ultra-low permeability in shales.The use of hybrid(combination)DCA models can improve results.However,complexities associated with these techniques can still make their application quite tedious without proper diagnostic plots,correct use of model parameters and some knowledge of the production mechanisms involved.This work,therefore,presents a new statistical data-driven approach of forecasting production from LRS reservoirs called the Principal Components Methodology(PCM).PCM is a technique that bypasses a lot of the difficulties associated with existing methods of forecasting and forecasts production with reasonable certainty.PCM is a data-driven method of forecasting based on the statistical technique of principal components analysis(PCA).In our study,we simulated production of fluids with different compositions for 30 years with the aid of a commercial compositional simulator.We then applied the Principal Components Methodology(PCM)to the production data from several representative wells by using Singular Value Decomposition(SVD)to calculate the principal components.These principal components were then used to forecast oil production from wells with production histories ranging from 0.5 to 3 years,and the results were compared to simulated data.Application of the PCM to field data is also included in this work.

关键词： Principal components Liquid-rich shale Unconventional resources production forecasting Pattern recognition

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Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection

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Petroleum Exploration and Development 2020年第2期47卷 383-392页

作者： NEGASH Berihun Mamo YAW Atta Dennis University Teknologi PETRONAS Petroleum Engineering Department32610 Seri IskandarPerak Darul RidzuanMalaysia

As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.

关键词： neural networks machine learning attribute extraction Bayesian regularization algorithm production forecasting water flooding

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Application of ARIMA-RTS optimal smoothing algorithm in gas well production prediction

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Petroleum 2022年第2期8卷 270-277页

作者： Yonggang Duan Huan Wang Mingqiang Wei Linjiang Tan Tao Yue School of Petroleum and Natural Gas Engineering Southwest Petroleum UniversityChengdu610500China School of Mechanical and Electrical Engineering Southwest Petroleum UniversityChengdu610500China

Gas field production forecast is an important basis for decision-making in the gas industry.How to accurately predict the dynamic production during gas field development is an important content of reservoir engineering research.Reservoir numerical simulation is the most common method for predicting oil and gas production.However,it requires a lot of data to build an accurate geological model which is tedious and time-consuming.At present,many scholars have used machine learning and data mining methods to predict oil and gas production,but they have not considered whether the use of increasing production measures will affect the predicted results.Thus,ARIMA-RTS optimal smooth algorithm is the first applied to establish the prediction model of gas well production.According to the historical production data,the model is processed,the production differential autoregressive integral moving average(ARIMA)model in time series is established,then ARIMA model is combined with RTS(Rauch Tung Striebel)smoothing,and the production prediction model is constructed.RTS smoothing algorithm is an enhanced version of Kalman filter.The measurements are firstly processed by the forward filter,and then,a separate backward smoothing pass is used for obtaining the smoothing solution.The correctness of ARIMA-RTS model was verified with the actual production data.The results show that the prediction based on ARIMA-RTS model can accurately reflect the production performance of gas well.This method can effectively reduce the error caused by stimulation when predicting.When using the ARIMA-RTS model and the ARIMA-Kalman model to predict the production of the same gas well,the prediction accuracy of ARIMA-RTS model is higher than that of ARIMA-Kalman model in production wells with stimulation.Compared with that of the ARIMA-Kalman model,the mean relative error fitted by the ARIMA-RTS model is reduced by 46.3%,and the relative mean square error is reduced by 56.48%.ARIMA-RTS optimal smooth algorithm improves the prediction accuracy of gas wel

关键词： production forecasting ARIMA RTS Kalman filter

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Deep-Learning-Based production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models

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Computer Modeling in Engineering & Sciences 2022年第6期130卷 1579-1599页

作者： Shaohua Gu Jiabao Wang Liang Xue Bin Tu Mingjin Yang Yuetian Liu State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development Beijing100083China Sinopec Petroleum Exploration and production Research Institute Beijing100083China Department of Oil-Gas Field Development Engineering College of Petroleum EngineeringChina University of PetroleumBeijing102249China

production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery,which has an important impact on gas field development planning and economic evaluation.Owing to the model’s simplicity,the decline curve analysis method has been widely used to predict production performance.The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs.In this paper,a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed.The sequence learning methods used in production performance analysis herein include the recurrent neural network(RNN),long short-term memory(LSTM)neural network,and gated recurrent unit(GRU)neural network,and their performance in the tight gas reservoir production prediction is investigated and compared.To further improve the performance of the sequence learning method,the hyperparameters in the sequence learning methods are optimized through a particle swarm optimization algorithm,which can greatly simplify the optimization process of the neural network model in an automated manner.Results show that the optimized GRU and RNN models have more compact neural network structures than the LSTM model and that the GRU is more efficiently trained.The predictive performance of LSTM and GRU is similar,and both are better than the RNN and the decline curve analysis model and thus can be used to predict tight gas production.

关键词： Tight gas production forecasting deep learning sequence learning models

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forecasting solar still performance from conventional weather data variation by machine learning method

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Chinese Physics B 2023年第4期32卷 19-25页

作者：高文杰沈乐山孙森山彭桂龙申震王云鹏 AbdAllah Wagih Kandeal 骆周扬 A.E.Kabeel 张坚群鲍华杨诺 State Key Laboratory of Coal Combustion and School of Energy and Power EngineeringHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Baima Lake Laboratory Co. Ltd.Hangzhou 31121China Zhejiang Energy Group R&D Institute Co.Ltd.Hangzhou 311121China Zhejiang Zheneng Yueqing Electric Power Generation Co. Ltd.Yueqing 325609China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai 200240China Mechanical Engineering Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh 33516Egypt Mechanical Power Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Faculty of Engineering Delta University for Science and TechnologyGamasaEgypt

Solar stills are considered an effective method to solve the scarcity of drinkable water.However,it is still missing a way to forecast its production.Herein,it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data.The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm.The required data to train the model are obtained from daily measurements lasting9 months.To validate the accuracy model,the determination coefficients of two types of solar stills are calculated as 0.935and 0.929,respectively,which are much higher than the value of both multiple linear regression(0.767)and the traditional models(0.829 and 0.847).Moreover,by applying the model,we predicted the freshwater production of four cities in China.The predicted production is approved to be reliable by a high value of correlation(0.868)between the predicted production and the solar insolation.With the help of the forecasting model,it would greatly promote the global application of solar stills.

关键词： solar still production forecasting forecasting model weather data random forest

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