scholarly journals Correction to: Abiotic resource depletion potentials (ADPs) for elements revisited—updating ultimate reserve estimates and introducing time series for production data

2019 ◽  
Vol 25 (2) ◽  
pp. 309-310
Author(s):  
Lauran van Oers ◽  
Jeroen B. Guinée ◽  
Reinout Heijungs
Keyword(s):  
Time Series ◽  
Resource Depletion ◽  
Production Data ◽  
Abiotic Resource ◽  
Abiotic Resource Depletion ◽  
Reserve Estimates

scholarly journals Abiotic resource depletion potentials (ADPs) for elements revisited—updating ultimate reserve estimates and introducing time series for production data

2019 ◽  
Vol 25 (2) ◽  
pp. 294-308 ◽  
Author(s):  
Lauran van Oers ◽  
Jeroen B. Guinée ◽  
Reinout Heijungs
Keyword(s):  
Case Studies ◽  
Impact Category ◽  
Resource Depletion ◽  
Annual Production ◽  
Production Data ◽  
Cumulative Production ◽  
Abiotic Resource ◽  
Abiotic Resource Depletion ◽  
The Impact ◽  
Calculation Procedures

Abstract Purpose In 1995, the original method for assessing the impact category abiotic resource depletion using abiotic depletion potentials (ADPs) was published. The ADP of a resource was defined as the ratio of the annual production and the square of the ultimate (crustal content based) reserve for the resource divided by the same ratio for a reference resource (antimony (Sb)). In 2002, ADPs were updated based on the most recent USGS annual production data. In addition, the impact category was sub-divided into two categories, using two sets of ADPs: the ADP for fossil fuels and the ADP for elements; in this article, we focus on the ADP for elements. Since then, ADP values have not been updated anymore despite the availability of updates of annual production data and also updates of crustal content data that constitute the basis of the ultimate reserves. Moreover, it was known that the coverage of elements by ADPs was incomplete. These three aspects together can affect relative ranking of abiotic resources based on the ADP. Furthermore, dealing with annually changing production data might have to be revisited by proposing new calculation procedures. Finally, category totals to calculate normalized indicator results have to be updated as well, because incomplete coverage of elements can lead to biased results. Methods We used updated reserve estimates and time series of production data from authoritative sources to calculate ADPs for different years. We also explored the use of several variations: moving averages and cumulative production data. We analyzed the patterns in ADP over time and the contribution by different elements in the category total. Furthermore, two case studies are carried out applying two different normalization reference areas (the EU 27 as normalization reference area and the world) for 2010. Results and discussion We present the results of the data updates and improved coverage. On top of this, new calculation procedures are proposed for ADPs, dealing with the annually changing production data. The case studies show that the improvements of data and calculation procedures will change the normalized indicator results of many case studies considerably, making ADP less sensitive for fluctuating production data in the future. Conclusions The update of ultimate reserve and production data and the revision of calculation procedures of ADPs and category totals have resulted in an improved, up-to-date, and more complete set of ADPs and a category total that better reflects the total resource depletion magnitude than before. An ADP based on the cumulative production overall years is most in line with the intent of the original ADP method. We further recommend to only use category totals based on production data for the same year as is used for the other (emission-based) impact categories.

Quantification of Uncertainty in Reserves Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs

2007 ◽  
Author(s):  
Yueming Cheng ◽  
W. John Lee ◽  
Duane A. McVay
Keyword(s):  
Oil And Gas ◽  
Curve Analysis ◽  
Production Data ◽  
Data Sets ◽  
Gas Wells ◽  
Advanced Technique ◽  
Unconventional Resources ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Reserve Estimates

Decline curve analysis is the most commonly used technique to estimate reserves from historical production data for evaluation of unconventional resources. Quantifying uncertainty of reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in analysis of unconventional oil or gas wells. Probabilistic approaches are sometimes used to provide a distribution of reserve estimates with three confidence levels (P10, P50 and P90) and a corresponding 80% confidence interval to quantify uncertainties. Our investigation indicates that uncertainty is commonly underestimated in practice when using traditional statistical analyses. The challenge in probabilistic reserves estimation is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. In this paper, we present an advanced technique for probabilistic quantification of reserve estimates using decline curve analysis. We examine the reliability of uncertainty quantification of reserve estimates by analyzing actual oil and gas wells that have produced to near-abandonment conditions, and also show how uncertainty in reserves estimates changes with time as more data become available. We demonstrate that our method provides more reliable probabilistic reserves estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.

scholarly journals Time series analysis and forecasting of China’s energy production during Covid-19: statistical models vs machine learning models

2021 ◽  
Author(s):  
Zekai Lu ◽  
Nian Liu ◽  
Ying Xie ◽  
Junhui Xu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Models ◽  
Energy Production ◽  
Production Data ◽  
Learning Models ◽  
Energy Research ◽  
Public Health Emergencies ◽  
Production Plans ◽  
Machine Learning Models

Abstract COVID-19 is a huge catastrophe of global proportions, and this catastrophe has had far-reaching effects on energy production worldwide. In this paper, we build traditional statistical models and machine learning models to forecast energy production series in the post-pandemic period based on Chinese energy production data and COVID-19 Chinese epidemic data from 2018 to 2021. The experimental results showed that the optimal models in this study outperformed the baseline models on each series, with MAPE values less than 10. Further studies found that the LightGBM, NNAT and LSTM machine learning models worked better in unstable energy series, while the ARIMA statistical model still had an advantage in stable energy time series. Overall, the machine learning models outperformed the traditional models during COVID-19 in terms of prediction. Our findings provide an important reference for energy research in public health emergencies, as well as a theoretical basis for factories to adjust their production plans and governments to adjust their energy decisions during COVID-19.

DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik

2020 ◽  
Vol 13 (02) ◽  
pp. 1-8
Author(s):  
Agrienvi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Production Data ◽  
Policy Makers ◽  
Strategic Value ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

scholarly journals Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6307
Author(s):  
Cong Wang ◽  
Lisha Zhao ◽  
Shuhong Wu ◽  
Xinmin Song
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Feature Extraction ◽  
Multivariate Time Series ◽  
Oil Production ◽  
Carbonate Reservoir ◽  
Surveillance Data ◽  
Production Data ◽  
Low Permeability ◽  
Boosting Method

Predictive analysis of the reservoir surveillance data is crucial for the high-efficiency management of oil and gas reservoirs. Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. In this method, the prediction target is the decline rate of oil production at a given time for one well in the low-permeability carbonate reservoir. The input data to train the model includes reservoir production data (e.g., oil rate, water cut, gas oil ratio (GOR)) and reservoir operation data (e.g., history of choke size and shut-down activity) of 91 producers in this reservoir for the last 20 years. The tree boosting algorithm aims to quantitatively uncover the complicated hidden patterns between the target prediction parameter and other monitored data of a high variety, through state-of-the-art automatic classification and multiple linear regression algorithms. We also introduce a segmentation technique that divides the multivariate time-series production and operation data into a sequence of discrete segments. This feature extraction technique can transfer key features, based on expert knowledge derived from the in-reservoir surveillance, into a data form that is suitable for the machine learning algorithm. Compared with traditional methods, the approach proposed in this article can handle surveillance data in a multivariate time-series form with different strengths of internal correlation. It also provides capabilities for data obtained in multiple wells, measured from multiple sources, as well as of multiple attributes. Our application results indicate that this approach is quite promising in capturing the complicated patterns between the target variable and several other explanatory variables, and thus in predicting the daily oil production rate.

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