Prediction of Shale-Gas Production at Duvernay Formation Using Deep-Learning Algorithm

Summary Decline–curve analysis (DCA) is an easy and fast empirical regression method for predicting future well production. However, applying DCA to shale–gas wells is limited by long transient flow, a unique completion design, and high–density drilling. Recently, a long short-term-memory (LSTM) algorithm has been widely applied to the prediction of time–series data. Because shale–gas–production data are time–series data, the LSTM algorithm can be applied to predict future shale–gas production. After information for 332 shale–gas wells in Alberta, Canada, is obtained from a commercial database, the data are preprocessed in seven steps, including cutoffs for well list, data cleaning, feature extraction, train and test sets split, normalization, and sorting for input into the LSTM model. The LSTM model is trained in 405 seconds by two features of production data and a shut–in (SI) period from 300 wells. The two–feature case shows a better prediction accuracy than both the one–feature case (i.e., production data only) and the hyperbolic DCA, where the three methods are tested on unseen data from 15 wells. The two–feature case can predict future production rates according to the SI period and provide a stable result for available time–series data.

Download Full-text

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

Agrienvi, Jurnal Ilmu Pertanian ◽

10.36873/aev.v13i02.723 ◽

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.

Download Full-text

Production Decline Prediction of Shale Gas using Hybrid Models

Global Journal of Researches in Engineering ◽

10.34257/gjrejvol20is5pg1 ◽

2020 ◽

pp. 1-16

Author(s):

P. Manda ◽

D.B. Nkazi

Keyword(s):

Shale Gas ◽

Time Series Data ◽

Moving Average ◽

Hybrid Models ◽

Gas Production ◽

Series Data ◽

Average Deviation ◽

Exponential Decline ◽

Ann Models ◽

Predicted Values

Hybrid models have frequently been used for shale gas production decline prediction by manipulating the unique strength of each of the known decline models. The use of a combination of models provides a more precise predicting model for forecasting time series data as compared to an individual model. In this study, the forecasting performance of decline curve hybrid models and ANN-ARIMA hybrid models are evaluated and compared with Arps’, Duong’s, the Power Law Exponential Decline, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neutral Network (ANN) models, respectively. The variable used to assess the models was the respective flow rate, q(t) monitored over a period of time (T). The results have shown that the single model approach can outperform hybrid models. The average deviation of the two best models indicates a central tendency of the production data around the mean. Subsequently, the spread in the data between the actual and predicted values is found to be less. It can thus be concluded that the ARIMA and ANN models have the best forecasting accuracy for production decline in shale gas compared to the other models.

Download Full-text

Pembentukan Harga Cabai Merah Keriting Secara Teoritis Dan Empiris

Paspalum: Jurnal Ilmiah Pertanian ◽

10.35138/paspalum.v9i2.283 ◽

2021 ◽

Vol 9 (2) ◽

pp. 104

Author(s):

Dety Sukmawati ◽

Euis Dasipah

Keyword(s):

Time Series ◽

Time Series Data ◽

Supply And Demand ◽

Price Formation ◽

Series Data ◽

Production Data ◽

Wholesale Market ◽

Price Information ◽

The West ◽

Information Center

High demand for curly red chilies will cause prices to rise while production cannot fulfill consumer desires. This situation was caused by an imbalance of supply-demand, where the supply-demand imbalance can be caused by several changes such as changes in production technology, population growth or number of consumers, changes in income levels per capita and season (., Asriani, and Rasyid 2012). Research data as research subjects were 1) Price time series data, curly red chili production at production centers 2) Supply data of curly red chilies from Cikajang Garut Regency, Caringin Central Market, Gedebage Main Market and Kramat Jati Central Market, 3) Time series data price, production, supply, government policies and supporting data from the West Java Food Crops Agriculture Office, and related agencies. The data used were time series data and supporting data from: Price information centers in production centers, main markets and price information at the West Java Food Crops Agricultural Service, for each marketing agency data was carried out by tracing the marketing chain. The research analysis was carried out in several ways, namely theoretically and empirically at the production center and the wholesale market described descriptively. Theoretical price formation can be explained that prices was formed based on supply and demand. Prices derived from price formation can come from the District or Provincial Agriculture Office and be informed from the Commodity Price Information Center in production centers and forwarded to farmers, dealers, traders and wholesale markets. Price information can be conveyed to between market players, so that farmers and market players know your margin and profit. Empirically, it can be seen that price formation in production centers was not seen to be formed from supply and demand. The price in the wholesale market is the price determined by market players in the wholesale market based on the amount of supply entering the main market and price information between the parent markets. The information center at PIKJ does not have production data from production centers so that when the price hike occurs, the version of the Ministry of Agriculture is imports of chilies ("specifically for curly red chilies, there are no imports"). Imports indicate that the production / supply decreases without knowing the actual amount of production, in this case the price information speed was faster than the production data that was informed per year so that prices in farmers still do not increase, meaning that farmers do not enjoy price increases, in this case it can be said that market mechanism was not working well.

Download Full-text

Permodelan Produksi Kopi Indonesia dengan Menggunakan Seasonal Autoregressive Integrated Moving Average (SARIMA)

Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam ◽

10.32493/jsmu.v2i1.2914 ◽

2019 ◽

Vol 2 (1) ◽

pp. 1

Author(s):

Hutomo Atman Maulana ◽

Kasuma Wardany Harahap ◽

Adriyansyah Adriyansyah ◽

Rofiroh Rofiroh ◽

Fuad Zainuddin

Keyword(s):

Time Series ◽

Data Processing ◽

Time Series Data ◽

Moving Average ◽

Original Data ◽

Series Data ◽

Production Data ◽

Coffee Production ◽

Autoregressive Integrated Moving Average ◽

Seasonal Data

This research used a method in modelling time series data in the form of seasonal data. The method used in this study is the Seasonal Autoregressive Integrated Moving Average (SARIMA). This method is applied to Indonesian coffee production data from January 2009 - December 2013 with the aim of obtaining a model that will be used to predict the amount of coffee production in January 2014 - December 2014. The forecasting results from the next model will be compared with the original data. Data processing is done using EViews software. Based on the results of data processing, the best model for forecasting is obtained, SARIMA (2,1,0) (1,1,1)12

Download Full-text

Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells

10.2118/195905-ms ◽

2019 ◽

Author(s):

Fahd Saghir ◽

M. E. Gonzalez Perdomo ◽

Peter Behrenbruch

Keyword(s):

Time Series ◽

Coal Seam ◽

Time Series Data ◽

Innovative Approach ◽

Abnormal Behavior ◽

Series Data ◽

Gas Wells ◽

Coal Seam Gas

Download Full-text

Frits Fahridws Damanik

Agrienvi, Jurnal Ilmu Pertanian ◽

10.36873/aev.v13i02.657 ◽

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 sothat must 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 appliedbased on the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in CentralKalimantan Province is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

Download Full-text

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

Download Full-text

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Download Full-text