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

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.

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

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.

Forecasting of China's natural gas production and its policy implications

With the vigorous promotion of energy conservation and implementation of clean energy strategies,China＇s natural gas industry has entered a rapid development phase,and natural gas is playing an increasingly important role in China＇s energy structure.This paper uses a Generalized Weng model to forecast Chinese regional natural gas production,where accuracy and reasonableness compared with other predictions are enhanced by taking remaining estimated recoverable resources as a criterion.The forecast shows that China＇s natural gas production will maintain a rapid growth with peak gas of 323 billion cubic meters a year coming in 2036;in 2020,natural gas production will surpass that of oil to become a more important source of energy.Natural gas will play an important role in optimizing China＇s energy consumption structure and will be a strategic replacement of oil.This will require that exploration and development of conventional natural gas is highly valued and its industrial development to be reasonably planned.As well,full use should be made of domestic and international markets.Initiative should also be taken in the exploration and development of unconventional and deepwater gas,which shall form a complement to the development of China＇s conventional natural gas industry.

Where is the future of China's biogas? Review, forecast,and policy implications

This paper discusses the history and present status of different categories of biogas production in China,most of which are classified into rural household production,agriculture-based engineering production,and industry-based engineering production.To evaluate the future biogas production of China,five models including the Hubbert model,the Weibull model,the generalized Weng model,the H-C-Z model,and the Grey model are applied to analyze and forecast the biogas production of each province and the entire country.It is proved that those models which originated from oil research can also be applied to other energy sources.The simulation results reveal that China＇s total biogas production is unlikely to keep on a fast-growing trend in the next few years,mainly due to a recent decrease in rural household production,and this greatly differs from the previous goal set by the official department.In addition,China＇s biogas production will present a more uneven pattern among regions in the future.This paper will give preliminary explanation for the regional difference of the three biogas sectors and propose some recommendations for instituting corresponding policies and strategies to promote the development of the biogas industry in China.

How much Numerical Products Affect Weather Forecasting

The paper shows how much improvement can be achieved in weather forecasting by using NWP products. And for weather element forecasts, the types and number of NWP products highly impact on the quality of MOS forecasts and other utilities.

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

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.

Production Forecast of Citrus in China and Production and Marketing Situation of Citrus in Chongqing in 2016 Production Season

According to the statistics of the Ministry of Agriculture,the planting area of citrus would increase steadily,and the yield would decline slightly,2. 556 7 million ha and 36. 168 million t,respectively. Compared with 2015,the planting area would increase by 1. 97% and the yield would increase by 1. 17%. According to the production scheduling of Chongqing Agricultural Commission,the citrus production in Chongqing in 2016 would continue to maintain a steady and rapid growth,the estimated area and yield were 0. 206 7 million ha and 2. 8 million t,increasing by 4. 27% and 4. 48% compared with 2015 respectively. By the end of November 2016,most of mature citrus products in Chongqing would show different degree of rise in purchasing price,while the purchasing price of red orange and some processed raw material fruits would show different amplitude of decline. On the whole,the production and marketing situation of Chongqing citrus would become better.

Forecasting on Indonesian Coal Production and Future Extraction Cost: A Tool for Formulating Policy on Coal Marketing

Since 2011 Indonesia has become the world’s largest exporter of steam coal. Two supporting factors of Indonesia to be the largest exporter are its enormous production and low operating cost. This paper foresees the production and extraction cost of Indonesian coal in the coming period to evaluate marketing policies and estimate the cost of Indonesian coal supply in domestic market as well as in export market. The production forecasting is carried out by Gompertz curve. Peak production of Indonesian coal is expected to take place in 2026. Moreover, the extraction cost in coal basins which produce high calorific value of coal, in accordance to the operating cost forecasting, is higher than the one with low calorific value of coal due to its higher stripping ratio. Three main basins of Central Sumatra, Tarakan, and Barito basins play major rule in supplying coal for domestic use in short term. And other coal basins such as South Sumatra, Kutai, Bengkulu, and Ombilin basins provide long term supply in the domestic and export markets.

Development and Application of Meteorological Disaster Monitoring and Early Warning Platform for Characteristic Agriculture in Huzhou City Based on GIS

Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for characteristic agriculture in Huzhou City. This platform integrates the functions of meteorological and agricultural information monitoring,disaster identification and early warning,fine weather forecast product display,and data query and management,which effectively enhances the capacity of meteorological disaster monitoring and early warning for characteristic agriculture in Huzhou City,and provides strong technical support for the meteorological and agricultural departments in the agricultural meteorological services.

The Value Constrained Production Decline Model and Its Application in Reserve Valuation

In the asset valuation of oil and gas reserves, it is discovered that the production decline trend of wells is not very obvious and that it is hard to make a production forecast matching the production history, thus resulting in a significant deviation of oil and gas asset value. For production with a significant fluctuation, the value deviation is also considerable if the matching production, which is predicted with classical decline methods, cannot appropriately reflect the time value distribution of actual production. To mitigate such a deviation, a concept is proposed concerning the value constrained production forecast and the value constrained production decline model is developed. A field case is demonstrated as an application of such a model. The model can significantly decrease the risk in the value deviation of a production decline analysis and be applied to the production forecasts for a single well, well clusters, blocks or field scale, and even for other mining industries.

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

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.

Forecasting oil production in unconventional reservoirs using long short term memory network coupled support vector regression method: A case study

Production prediction is crucial for the recovery of hydrocarbon resources.However,accurate and rapid production forecasting remains challenging for unconventional reservoirs due to the complexity of the percolation process and the scarcity of available data.To address this problem,a novel model combining a long short-term memory network(LSTM)and support vector regression(SVR)was proposed to forecast tight oil production.Three variables,the tubing head pressure,nozzle size,and water rate were utilized as the inputs of the presented machine-learning workflow to account for the influence of operational parameters.The time-series response of tight oil production was the output and was predicted by the optimized LSTM model.An SVR-based residual correction model was constructed and embedded with LSTM to increase the prediction accuracy.Case studies were carried out to verify the feasibility of the proposed method using data from two wells in the Ma-18 block of the Xinjiang oilfield.Decline curve analysis(DCA)methods,LSTM and artificial neural network(ANN)models were also applied in this study and compared with the LSTM-SVR model to prove its superiority.It was demonstrated that introducing residual correction with the newly proposed LSTM-SVR model can effectively improve prediction performance.The LSTM-SVR model of Well A produced the lowest prediction root mean square error(RMSE)of 5.42,while the RMSE of Arps,PLE Duong,ANN,and LSTM were 5.84,6.65,5.85,8.16,and 7.70,respectively.The RMSE of Well B of LSTM-SVR model is 0.94,while the RMSE of ANN,and LSTM were 1.48,and 2.32.

Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir.This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir.The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity,extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity.This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multifactors and complex mechanism.The study result shows that this method is a practical,effective,accurate and indirect productivity forecast method and is suitable for field application.

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

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.

The Fuzzy Control Forecast of Fully-Mechanized Coal Face Production Capacity

In this paper, the fully\|mechanized coal face system is thought of as a fuzzy controller, the various factors that have effect on the controller are found and analysis has been made as to how they effect the fully\|mechanized coal face′s production capacity. Based on the above analysis, this paper establishs a series of data analysis models describing the quantitative characteristics of the fully\|mechanized coal face production system. With this series of data models, 90 fully\|mechanized coal faces are processed and the fuzzy control forecasting model of the fully\|mechanized coal faces production capacity is established. This model is accurate and reliable and has achieved good results in practical applicaton.

Effect of introducing weather parameters on the accuracy of milk production forecast models

The objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models.The two models chosen were the nonlinear auto-regressive model with exogenous input(NARX)and the multiple linear regression(MLR)model.The accuracy of these models were assessed using seven different combinations of precipitation,sunshine hours and soil temperature as additional model training inputs.Lactation data(daily milk yield and days in milk)from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database.The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short(10-day),medium(30-day)and long-term(305-day)forecast horizons.The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield(kg),with R2 values greater than 0.7 for 95.5%and 14.7%of total predictions,respectively.The results showed that the introduction of sunshine hours,precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short,medium and long-term forecast horizons.Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60%and 70%of all predictions(for all 39 test cows from both groups).However,the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%.Thus,the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy.

Is it time to shake off the quota haze? The Forecast of Original Restrained Textile Products Export from China to the United States

Chinese textile companies might see Thursday--the first day of 2009--as the beginning of an unfettered era of free trade,but the outlook is clouded by weakening demand and rising protectionism amid the financial crisis. Many textile companies cut exports to the lowest level in recent years.Some even failed to use up the quotas acquired before the third quarter.Shrinking orders in the fourth quarter and dim prospects for the year 2009 would mean a"tight"year for textile makers.This report contains information on the export status of original US quota-

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

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 well that uses stimulation.We therefore conclude that the ARIMA-RTS optimal smooth algorithm can help us better forecast the forecasting gas well production with stimulation,as well as other fuels output.