Robust Decline Curve Analysis

Darwis, Sutawanir; Ruchjana, Budi Nurani; Permadi, Asep Kurnia

doi:10.22342/jims.15.2.50.105-111

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…They presented a more accurate approach by combining exponential and hyperbolic declines into one new model. Darwis et al (2009) used DCA to develop three models by using historical data and least squares techniques. Peak oil models forecast is another way of predicting future oil production and oil reserves, several models are available that uses this approach (Lianyoung et al (2008); Wang et al (2011)).…”

Section: Literature Reviewmentioning

confidence: 99%

Product Mix Optimization for an Oil Field Operating Company

Khonji¹,

Suliman²

2020

MAS

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In this study, a mathematical model is formulated to select the optimal product mix of wells in terms of numbers and types of wells that helps to maximize profit. The optimization model comprises two main components, the first component is revenue which includes forecasting of production and oil price, and the second component is cost which includes capital and operating costs. In addition, the model considers all related constraints such as budget, production targets, surface facility limitations, drilling rigs availability and others. Time has influence on the model, since its output is not limited only to the types and numbers of wells to be drilled during the planned period, but also when each well to be drilled for the same plan. Actual planning data for three consecutive years is used for model testing. The results show that 42% to 47% cost saving can be achieved by using the model. The analysis shows that with every 10% increase in oil price, the profit increases by about 6%. Also, it shows that the number of rigs and the rig daily cost affect the profit tremendously, where by reducing these two parameters by 50% an increase of 66% in oil profit can be achieved. The study confirms that oil field operating companies can stand a better chance of maximizing their profit by using product mix optimization model to define the optimum schedule for the number of wells, type of wells and time of drilling.

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Section: Literature Reviewmentioning

confidence: 99%

Product Mix Optimization for an Oil Field Operating Company

Khonji¹,

Suliman²

2020

MAS

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“…The use of a Duong power law model and the Valko stretched-exponentdecline model (SEDM) were compared with a conclusion that a systematic comparison of results from multiple models would be beneficial to assess uncertainty in the estimate of reserves(Denney, 2012). The addition of a regression analysis technique to the standard method of least squares fit for the exponential decline curve can provide a better fit and representation of historical production(Darwis et al, 2009). Finally, it has been found that the application of a logistic growth model, originally designed to project population growth, produces realisticDownloaded by [Umeå University Library] at 18:25 18 November 2014…”

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confidence: 99%

Using growth and decline factors to project VOC emissions from oil and gas production

Oswald

Harper

Barickman

et al. 2014

Journal of the Air & Waste Management Association

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Projecting future-year emission inventories in the oil and gas sector is complicated by the fact that there is a life cycle to the amount of production from individual wells and thus from well fields in aggregate. Here we present a method to account for that fact in support of regulatory policy development. This approach also has application to air quality modeling inventories by adding a second tier of refinement to the projection methodology. Currently, modeling studies account for the future decrease in emissions due to new regulations based on the year those regulations are scheduled to take effect. The addition of a year-by-year accounting of production decline provides a more accurate picture of emissions from older, uncontrolled sources. This proof of concept approach is focused solely on oil production; however, it could be used for the activity and components of natural gas production to compile a complete inventory for a given area.

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Petroleum Production Forecasting Using Machine Learning Algorithms

Ojedapo

Ikiensikimama

Wachikwu-Elechi

2022

Day 3 Wed, August 03, 2022

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Field forecasting and predictive production analysis has tremendous effects on assets planning and allocation and it cannot be over emphasized. For accurate actions to be taken, appropriate forecasts need to be made on each asset, hence the need to develop methods to aid the process. As a tilt from the conventional methodology of forecasting involving use of curve fitting techniques, and multi-level computational analysis, data driven approaches can be employed. This study presents the applications of data driven approaches to forecast production. Deep learning neural network algorithm and statistics- based data driven approach were considered. An LSTM model was developed and for the statistical algorithms, an ARIMA model, and a Holt Winters model was developed. The models were deployed, and the performance of the models were checked to determine more accurate approach for forecasting. Error analysis on the results form the models showed that the deep learning neural network model provided better results in comparison to the statistical models with an MAE of 0.0328. Based on the model performances, LSTM model can be considered for use in forecasting petroleum production overcoming effects of seasonal changes, and production anomalies in the life of the reservoir.

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Robust Decline Curve Analysis

Cited by 4 publications

References 3 publications

Product Mix Optimization for an Oil Field Operating Company

Product Mix Optimization for an Oil Field Operating Company

Using growth and decline factors to project VOC emissions from oil and gas production

Petroleum Production Forecasting Using Machine Learning Algorithms

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