Production Forecasting with Logistic Growth Models

Clark, Aaron J.; Lake, Larry W.; Patzek, Tadeusz W

doi:10.2118/144790-ms

Cited by 125 publications

(68 citation statements)

References 12 publications

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“…In an ideal situation, these properties can be used to define the drainage volume of a well. It therefore seems reasonable, as suggested by Clark et al (2011), to use the carrying capacity as a constraint on the recoverable reserves from a well.…”

Section: Discussionmentioning

confidence: 99%

“…Clark et al (2011) presented the first application of the logistic growth model in production forecasting in unconventional reservoirs. The logistic growth model is given as…”

Section: Model-based Analysismentioning

confidence: 99%

“…Clark et al (2011) introduced the logistic growth model, which has the desirable property of yielding physical values of reserves recovery. Ilk (2010), after analyzing production (rate) data from shale gas wells, developed a power-law model and a method of applying the model that arbitrarily switched from the power-law decline behavior to an exponential decline behavior.…”

Section: Introductionmentioning

confidence: 99%

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An approach to modeling production decline in unconventional reservoirs

Ogunyomi

Dong

et al. 2017

J Petrol Explor Prod Technol

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Most decline curve methods have two main limitations; the model parameters as a rule are not functions of reservoir parameters and may yield unrealistic (nonphysical) values of expected ultimate recovery (EUR) because boundary-dominated flow may not develop in unconventional reservoirs. Over the past few years, several empirical models have emerged to address the second limitation, but they are challenged by the time to transition from infinite-acting flow period to the boundary-dominated flow. In this study, we performed statistical and modelbased analysis of production data from hydraulically fractured horizontal oil wells and present a method to mitigate some of the limitations highlighted above. The production data were carefully analyzed to identify the flow regimes and understand the overall decline behavior. Following this step, we performed model-based analysis using the parallel flow model (sum of exponential terms), and the logistic growth model. After the model-based analysis, the model parameters were analyzed statistically and cross-plotted against available reservoir and well completion parameters. Based on the conclusion from the cross-plots and statistical analysis, we used design of experiments (DoE) and numerical reservoir simulations to develop functions that relate the model parameters and reservoir/well completion properties. Results from this work indicate that the production characteristics from these wells are highly variable. In addition, the parallel flow model indicates that there are at least two to three different time domains in the production behavior and that they are not the result of operational changes, such as well shut-in or operating pressure changes at the surface. All the models used in this study provide very good fits to the data, and all provide realistic estimates of EUR. The cross-plots of model parameters and some reservoir/well completion properties indicate that there is some relationship between them, which we developed using DoE and flow simulations. We have also shown how these models can be applied to obtain realistic estimates of EUR from early-time production data in unconventional oil reservoirs.

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Section: Discussionmentioning

confidence: 99%

“…Clark et al (2011) presented the first application of the logistic growth model in production forecasting in unconventional reservoirs. The logistic growth model is given as…”

Section: Model-based Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An approach to modeling production decline in unconventional reservoirs

Ogunyomi

Dong

et al. 2017

J Petrol Explor Prod Technol

Self Cite

View full text Add to dashboard Cite

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“…The rat livers in their experiment were reduced to one-third of their original sizes, and appeared to regenerate hyperbolically. Clark et al [39] adopted this model for shale gas reservoirs with extremely low permeability, and developed the Logistic Growth Model (LGM) as an empirical method to forecast the gas production. The cumulative production in this model is up to a maximum carrying capacity and there is no further growth after reaching this maximum value.…”

Section: Logistic Growth Modelmentioning

confidence: 99%

“…The major advantage of the LGM is that the reserve estimate is constrained by the parameter K as well as the production rate, which terminates at infinite time [39]. However, an upward inflection in the curve would occur if n > 1 [28].…”

Section: Logistic Growth Modelmentioning

confidence: 99%

Methods of Decline Curve Analysis for Shale Gas Reservoirs

2018

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Abstract:With help from horizontal wells and hydraulic fracturing, shale gas has made a significant contribution to the energy supply. However, due to complex fracture networks and complicated mechanisms such as gas desorption and gas slippage in shale, forecasting shale gas production is a challenging task. Despite the versatility of many simulation methods including analytical models, semi-analytical models, and numerical simulation, Decline Curve Analysis has the advantages of simplicity and efficiency for hydrocarbon production forecasting. In this article, the eight most popular deterministic decline curve methods are reviewed: Arps, Logistic Growth Model, Power Law Exponential Model, Stretched Exponential Model, Duong Model, Extended Exponential Decline Model, and Fractural Decline Curve model. This review article is dedicated to summarizing the origins, derivations, assumptions, and limitations of these eight decline curve models. This review article also describes the current status of decline curve analysis methods, which provides a comprehensive and up-to-date list of Decline Curve Analysis models for petroleum engineers in analysis of shale gas reservoirs. This work could serve as a guideline for petroleum engineers to determine which Decline Curve models should be applied to different shale gas fields and production periods.

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Moving boundary approach to forecast tight oil production

2020

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A novel semianalytical production predictive tool for tight reservoirs based on the application of material balance on a transient linear flow system is developed in this paper. This method considers two important regions during transient production of oil reservoirs: the saturated region where gas evolves and flows with oil, and the undersaturated region where only oil flows. A zonal moving boundary approach is used to evolve the two regions as the reservoir pressure gradually decreases. A semianalytical method is used to calculate pressures in the various regions and volumetric expansions. For both black oil and volatile oil scenarios, calculations from this analytical framework are able to match reservoir pressures, oil and gas rates, and cumulative gas–oil ratios determined using a reservoir simulator. The model was also applied to wells in tight reservoirs around the United States such as the Bakken (ND) and the Eagle Ford (TX) with reasonable success.

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Production Forecasting with Logistic Growth Models

Cited by 125 publications

References 12 publications

An approach to modeling production decline in unconventional reservoirs

An approach to modeling production decline in unconventional reservoirs

Methods of Decline Curve Analysis for Shale Gas Reservoirs

Moving boundary approach to forecast tight oil production

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