Multiple Well Deconvolution

Cumming, Jonathan A.; Wooff, David; Whittle, T.M.; Gringarten, Alain C.

doi:10.2118/166458-ms

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…Next steps include developing faster methods to compute the response and, more substantially, the extension to the multi-well case (Cumming et al, 2013b).…”

Section: Discussionmentioning

confidence: 99%

A Bayesian Multi-Region Radial Composite Reservoir Model for Deconvolution in Well Test Analysis

Botsas

Cumming

Jermyn

2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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In petroleum well test analysis, deconvolution is used to obtain information about reservoir systems, for example the presence of heterogeneities and boundaries. This information is contained in the response function, which can be estimated by solving an inverse problem in the pressure and flow rate measurements. Our Bayesian approach to this problem is based upon a parametric physical model of reservoir behaviour, derived from the solution for fluid flow in a general class of reservoirs. This permits joint parametric Bayesian inference for both the reservoir parameters and the true pressure and rate values, which is essential due to the typical observational error levels. Using sets of flexible priors for the reservoir parameters to restrict the solution space to physical behaviours, samples from the posterior are generated using Markov Chain Monte Carlo. Summaries and visualisations of the posterior, response, and true pressure and rate values can be produced, interpreted, and model selection can be performed. The method is validated through a synthetic application, and applied to a field data set. The results are comparable to the state of the art solution, butThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Next steps include developing faster methods to compute the response and, more substantially, the extension to the multi-well case (Cumming et al, 2013b).…”

Section: Discussionmentioning

confidence: 99%

A Bayesian Multi-Region Radial Composite Reservoir Model for Deconvolution in Well Test Analysis

Botsas

Cumming

Jermyn

2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…Substituting (12) into (11) and then taking the inverse Laplace transform of (11) yield the beta deconvolution formula:…”

Section: -Deconvolution and Its Modification Van Everdingen And Hurstmentioning

confidence: 99%

“…Thus combining sandface flow rate with pressure measurements will enhance the conventional (including type curve) well test interpretation methods. On the other hand, mathematically, deconvolution is also a highly unstable inverse problem because small errors in the data can result in large uncertainties in the deconvolution solution [10,11].…”

Section: Introductionmentioning

confidence: 99%

Explicit Deconvolution of Well Test Data Dominated by Wellbore Storage

Razminia

Hashemi

Razminia

et al. 2014

Abstract and Applied Analysis

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This paper addresses some methods for interpretation of oil and gas well test data distorted by wellbore storage effects. Using these techniques, we can deconvolve pressure and rate data from drawdown and buildup tests dominated by wellbore storage. Some of these methods have the advantage of deconvolving the pressure data without rate measurement. The two important methods that are applied in this study are an explicit deconvolution method and a modification of material balance deconvolution method. In cases with no rate measurements, we use a blind deconvolution method to restore the pressure response free of wellbore storage effects. Our techniques detect the afterflow/unloading rate function with explicit deconvolution of the observed pressure data. The presented techniques can unveil the early time behavior of a reservoir system masked by wellbore storage effects and thus provide powerful tools to improve pressure transient test interpretation. Each method has been validated using both synthetic data and field cases and each method should be considered valid for practical applications.

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“…Within the lumped parameter model, the continuous-time transfer function noise (TFN) modeling is widely used to simulate the hydrological system output in response to external forcing (von Asmuth et al, 2002). This approach employs a response function along with the convolution technique to represent various external forcing factors, such as recharge events or pumping activities (Ahmadi et al, 2017;Chakrabarty & Enachescu, 1997;Cumming et al, 2013;Gringarten, 2008;Hwang & Lu, 2007;Kuchuk et al, 1990;Levitan, 2003;Osman et al, 2017;Roumboutsos & Stewart, 1988). The groundwater level data might be noisy due to a combination of different external forcings.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Groundwater Time Series With Limited Pumping Information in Unconfined Aquifer: Response Function Based on Lagging Theory

Lin,

Tseng,

Lee

et al. 2024

Water Resources Research

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Groundwater extraction from aquifers is a common practice for human use, and variations in groundwater levels can provide valuable information on the hydrogeological properties of the aquifer. However, reliable data on pumping rates and distribution are often lacking due to unsupervised groundwater pumping activities. This study presents a new mathematical model for transfer function modeling that depicts the drawdown response caused by pumping in an unconfined aquifer system. To account for the dense and unsupervised pumping events, the uniform pumping approach was used to estimate these effects. To more accurately represent unconfined flow, the model first integrates lagging theory into a response function derived from the Boussinesq equation. The lagging theory accounts for the effects of both inertial force and capillary suction. Furthermore, the model has been used to derive both specific yield and transmissivity along with two lagging parameters simultaneously using only groundwater level information from the Choshui River region in Taiwan. The estimated results suggest that this approach provides reliable estimates of hydrogeological parameters, demonstrating its usefulness for water resource management and water budget evaluation.

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Multiple Well Deconvolution

Cited by 10 publications

References 10 publications

A Bayesian Multi-Region Radial Composite Reservoir Model for Deconvolution in Well Test Analysis

A Bayesian Multi-Region Radial Composite Reservoir Model for Deconvolution in Well Test Analysis

Explicit Deconvolution of Well Test Data Dominated by Wellbore Storage

Analysis of Groundwater Time Series With Limited Pumping Information in Unconfined Aquifer: Response Function Based on Lagging Theory

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