The Evaluation and Sensitivity of Decline Curve Modelling

Manda, Prinisha; Nkazi, Diakanua

doi:10.3390/en13112765

Cited by 17 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ARIMA model can distinguish patterns of time series well but not data patterns that are nonlinear, whereas ANN can only handle nonlinear data. Therefore, hybrid models link the benefits of ARIMA with respect to linear modeling and ANN in terms of nonlinear edge modeling, while, in certain conditions, the single-model approach can outperform hybrid models …”

Section: Model Overviewmentioning

confidence: 99%

“…An accuracy assessment has shown that decline curve modeling influences the estimated ultimate recovery (EUR) of SGRs and the different decline curve models result in different EUR results, which is either over- or underestimated . Previous research has shown that production time considerably influences the EUR, which is dependent on the decline curve model being used .…”

Section: Introductionmentioning

confidence: 99%

“…Previous research has shown that production time considerably influences the EUR, which is dependent on the decline curve model being used . It is apparent from the assessment that there are benefits in using existing DCA models; however, they also have constraints associated with them, which have to be addressed …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accuracy Assessment of Single and Hybrid Models for Predicting Shale Gas Production

Manda

Nkazi

2021

Energy Fuels

Self Cite

View full text Add to dashboard Cite

The accuracy of a predictive tool determines the levels of trust in the model and its attraction for commercial usage. The study examined the single and hybrid model approach for shale gas production. Multilayer perception artificial neural network (ANN), autoregressive integrated moving average (ARIMA), and Arps–power law exponential hybrid decline models were developed to predict shale gas production and compared with the already developed Arps decline and power law exponential (PLE) decline models. By a trial-and-error approach, a multilayer perception (MLP) network with four neurons in the hidden layer was attained in the ANN structure to predict shale gas production. While for the ARIMA model, the number of nodes that showed the best performance indicated (2,1,2) for the two sets of data. Evaluation of the root mean square error (RMSE) values for the models showed that the Arps–power law exponential hybrid decline model had a lower percentage error in conjunction with good accuracy. The study found the Arps–power law exponential hybrid decline model to be a good forecaster of shale gas production and that hybrid models do deliver better accuracy over single models. A future revision of model assumptions may improve its accuracy and make the Arps–power law exponential hybrid decline model an attractive predictive tool.

show abstract

Section: Model Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accuracy Assessment of Single and Hybrid Models for Predicting Shale Gas Production

Manda

Nkazi

2021

Energy Fuels

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, current reservoir simulation techniques that are used in forecasting conventional oil and gas wells still face some challenges when it comes to forecasting unconventional resources due to challenges in modeling [3], costly data acquisition [4], complex fracture network [5] and variation of fracture permeability [6]. Another approach used to forecast unconventional gas resources is decline curve analysis (DCA) in which different models have been developed and used over the years [7,8]. Some of these decline curves were developed from physical interpretation of gas flow [9,10] and some of them are developed empirically based on observations [11,12].…”

Section: Introductionmentioning

confidence: 99%

Towards Better Shale Gas Production Forecasting Using Transfer Learning

Raymond¹,

Montgomery²,

Williams³

2021

Preprint

View full text Add to dashboard Cite

Deep neural networks can generate more accurate shale gas production forecasts in counties with a limited number of sample wells by utilizing transfer learning. This paper provides a way of transferring the knowledge gained from other deep neural network models trained on adjacent counties into the county of interest. The paper uses data from more than 6000 shale gas wells across 17 counties from Texas Barnett and Pennsylvania Marcellus shale formations to test the capabilities of transfer learning. The results reduce the forecasting error between 11% and 47% compared to the widely used Arps decline curve model.

show abstract

“…Decline curve modeling has been used to predict production data where the curve is fitted to the data to estimate future points. In [15], different decline models are evaluated, namely the Exponential Decline Model (SEDM) and the Logistic Growth Model (LGM), followed by the Extended Exponential Decline Model (EEDM), the Power Law Exponential Model (PLE), Doung's Model, and the Arps Hyperbolic Decline Model.…”

mentioning

confidence: 99%