Prediction of Reservoir Performance Applying Decline Curve Analysis

Rahuma, Khulud; Mohamed, H.S.; Hissein, N.; Giuma, S.

doi:10.7763/ijcea.2013.v4.266

Cited by 20 publications

(9 citation statements)

References 22 publications

(1 reference statement)

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“…There are many techniques available in the industry used for this purpose, and the most popular one is Decline Curve Analysis (DCA). Rahuma et al (2012) and Rahuma et al (2013) adopted the Arps equations (Arps is the developer of the DCA model) for prediction of reservoir performance. Makinde et al (2012) indicated that DCA and Arps equations have limitation, and using either exponential or hyperbolic decline might be unrealistic in forecasting.…”

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 Arps equations are suitable for individual wells as well as the production from entire well fields and are a very reliable approach to extrapolate future production based on a historical data set (Khulud et al, 2013). There are limitations to the Arps equations and this is especially true when using this technique to project future production from unconventional oil and gas A c c e p t e d M a n u s c r i p t 10 growth estimates in tight shale formations that are within the known carrying capacity of the formation (Clark, 2011).…”

Section: Decline Curve Analysismentioning

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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“…Until 1945, Arps [20] proposed the most classical DCA model in which three parameters are used to fit the production data. The Arps model is simple and fast and widely used in conventional reservoirs [21], but it still suffers some limitations, including boundary-dominated flow regime [22,23], constant work condition [24], and overestimating the estimated ultimate recovery (EUR) [25]. Especially, these limitations can be exaggerated when applying them in unconventional reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs

Liu

Kim

et al. 2021

Geofluids

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Horizontal well fracturing technology is widely used in unconventional reservoirs such as tight or shale oil and gas reservoirs. Meanwhile, the potential of enhanced oil recovery (EOR) methods including huff-n-puff miscible gas injection are used to further increase oil recovery in unconventional reservoirs. The complexities of hydraulic fracture properties and multiphase flow make it difficult and time-consuming to understand the well performance (i.e., well production) in fractured shale reservoirs, especially when using conventional numerical methods. Therefore, in this paper, two methods are developed to bridge this gap by using the machine learning technique to forecast well production performance in unconventional reservoirs, especially on the EOR pilot projects. The first method is the artificial neural network, through which we can analyze the big data from unconventional reservoirs to understand the underlying patterns and relationships. A bunch of factors is contained such as hydraulic fracture parameters, well completion, and production data. Then, feature selection is performed to determine the key factors. Finally, the artificial neural network is used to determine the relationship between key factors and well production performance. The second is time series analysis. Since the properties of the unconventional reservoir are the function of time such as fluid properties and reservoir pressure, it is quite suitable to apply the time series analysis to understand the well production performance. Training and test data are from over 10000 wells in different fractured shale reservoirs, including Bakken, Eagle Ford, and Barnett. The results demonstrate that there is a good match between the available and predicated well performance data. The overall R values of the artificial neural network and time series analysis are both above 0.8, indicating that both methods can provide reliable results for the prediction of well performance in fractured shale reservoirs. Especially, when dealing with the EOR field cases, such as huff-n-puff miscible gas injection, Time series analysis can provide more accurate results than the artificial neural network. This paper presents a thorough analysis of the feasibility of machine learning in multiple fractured shale reservoirs. Instead of using the time-consuming numerical methods, it also provides a more robust way and meaningful reference for the evaluation of the well performance.

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Prediction of Reservoir Performance Applying Decline Curve Analysis

Cited by 20 publications

References 22 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

Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs

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