Production rate of multi-fractured wells modeled with Gaussian pressure transients

Weijermars, Ruud

doi:10.1016/j.petrol.2021.110027

Cited by 16 publications

(12 citation statements)

References 28 publications

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“…As the flow is into the fracture surface from both sides, we can translate Darcy flux q D into well head rate using q w = 2A q D /B with single-sided fracture area A and formation volume factor B. For the 1D diffusion of a pressure change imposed on a reservoir region by a hydraulically fractured well system, the well rate is then given by the following equation [15]:…”

Section: Key Equationsmentioning

confidence: 99%

“…Another major step for the purpose of the current analysis is to assume the initial well rate q i starts when the initial 3 Geofluids time t = t 1 expended the first unit of time (for example, 1 day in the case of daily production rates, as was used in all the case studies of this paper) and is given by [15,18]…”

Section: Key Equationsmentioning

confidence: 99%

“…Combined with the historical cumulative production, DCA tools give acceptable results for the estimate ultimate recovery (EUR) of shale wells. As pointed out at length in prior work related to the Gaussian DCA method [14,15], it differs from existing DCA formulas in that it is physicsbased and only uses the hydraulic diffusivity as a matching parameter while resulting in excellent history matches. The appendix explains separately that the Gaussian DCA should ideally be applied to the total fluid produced.…”

Section: Introductionmentioning

confidence: 99%

“…This paper highlights the strengths of the Gaussian solution of the diffusivity equation [14,15], as a newly developed hybrid method for production forecasting and advanced reservoir modeling, which combines ease of use with fast results. Using the basic GPT methodology, the new case studies presented here highlight in detail how even the production behavior of so-called unruly wells can be readily described by the new production forecasting and reserve estimation method.…”

Section: Introductionmentioning

confidence: 99%

“…Four such case study wells are analyzed, from three different US shale plays (one well in the Utica Formation, Ohio; one in the Eagle Ford Formation, East Texas; and two wells producing from the Wolfcamp Formation, West Texas). The solutions at the basis of GPT applications have been published in open source peer-reviewed journals [14,15], due to which no license fees are due in principle. However, for corporate applications requiring a slick interface and back-office support, new products are under development accompanied by efforts to found an organization that can serve corporate clients and others as a non-for-profit foundation.…”

Section: Introductionmentioning

confidence: 99%

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Production Forecasting of Unruly Geoenergy Extraction Wells Using Gaussian Decline Curve Analysis

Weijermars

2023

Geofluids

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Fast and rigorous well performance evaluation is made possible by new solutions of the pressure diffusion equation. The derived Gaussian pressure transient (GPT) solutions can be practically formulated as a decline curve analysis (DCA) equation for history matching of historic well rates to then forecast the future well performance and estimate the remaining reserves. Application in rate transient analysis (RTA) mode is also possible to estimate fracture half-lengths. Because GPT solutions are physics-based, these can be used for production forecasting as well as in reservoir simulation mode (by computing the spatial and temporal pressure gradients everywhere in the reservoir section drained by either an existing or a planned well). The present paper focuses on the physics-based production forecasting of so-called “unruly” wells, which at first seem to have production behavior noncompliant with any DCA curve. Four shale wells (one from the Utica, Ohio; one from the Eagle Ford Formation, East Texas; and two from the Wolfcamp Formation, West Texas) are analyzed in detail. Physics-based adjustments are made to the Gaussian DCA history matching process, showing how the production rate of these wells is fully compliant with the rate implied by the hydraulic diffusivity of the reservoir sections where these wells drain from.

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Section: Key Equationsmentioning

confidence: 99%

Section: Key Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Production Forecasting of Unruly Geoenergy Extraction Wells Using Gaussian Decline Curve Analysis

Weijermars

2023

Geofluids

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Probabilistic estimation of hydraulic fracture half-lengths: validating the Gaussian pressure-transient method with the traditional rate transient analysis-method (Wolfcamp case study)

Alvayed,

Khalid,

Dafaalla

et al. 2023

J Petrol Explor Prod Technol

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Despite significant advancements in geomodelling technologies, Accurately estimating hydraulic fracture half-length remains a challenging task. This paper introduces a detailed estimation approach using the Gaussian Pressure Transient (GPT) method, which is relatively new. The GPT method is iterative, ensuring fast convergence and providing reliable estimations of hydraulic fracture half-length b’ased on a predetermined hydraulic diffusivity value obtained from Gaussian Decline Curve Analysis (DCA). To validate the GPT results, production data from two case study wells in the Wolfcamp Shale Formation, located in the Midland Basin of West Texas, are utilized alongside the traditional Rate-Transient Analysis (RTA) method. Moreover, the GPT method offers the capability to probabilistically estimate hydraulic fracture half-lengths, presenting two innovative approaches to evaluate the robustness of this newly developed method for both deterministic and probabilistic estimations. The simulation results demonstrate a close correlation between the Gaussian method and micro-seismic fracture half-lengths, with separate confirmation from the classic RTA-method. Through the case studies presented in this paper, the GPT-method showcases its utility in estimating hydraulic fracture half-lengths for two Wolfcamp case study wells, effectively demonstrating the validity and practical applicability of this novel method.

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Estimation of fracture half-length with fast Gaussian pressure transient and RTA methods: Wolfcamp shale formation case study

Ibrahim,

Weijermars

2023

J Petrol Explor Prod Technol

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Accurate estimation of fracture half-lengths in shale gas and oil reservoirs is critical for optimizing stimulation design, evaluating production potential, monitoring reservoir performance, and making informed economic decisions. Assessing the dimensions of hydraulic fractures and the quality of well completions in shale gas and oil reservoirs typically involves techniques such as chemical tracers, microseismic fiber optics, and production logs, which can be time-consuming and costly. This study demonstrates an alternative approach to estimate fracture half-lengths using the Gaussian pressure transient (GPT) Method, which has recently emerged as a novel technique for quantifying pressure depletion around single wells, multiple wells, and hydraulic fractures. The GPT method is compared to the well-established rate transient analysis (RTA) method to evaluate its effectiveness in estimating fracture parameters. The study used production data from 11 wells at the hydraulic fracture test site 1 in the Midland Basin of West Texas from Upper and Middle Wolfcamp (WC) formations. The data included flow rates and pressure readings, and the fracture half-lengths of the 11 wells were individually estimated by matching the production data to historical records. The GPT method can calculate the fracture half-length from daily production data, given a certain formation permeability. Independently, the traditional RTA method was applied to separately estimate the fracture half-length. The results of the two methods (GPT and RTA) are within an acceptable, small error margin for all 5 of the Middle WC wells studied, and for 5 of the 6 Upper WC wells. The slight deviation in the case of the Upper WC well is due to the different production control and a longer time for the well to reach constant bottomhole pressure. The estimated stimulated surface area for the Middle and Upper WC wells was correlated to the injected proppant volume and the total fluid production. Applying RTA and GPT methods to the historic production data improves the fracture diagnostics accuracy by reducing the uncertainty in the estimation of fracture dimensions, for given formation permeability values of the stimulated rock volume.

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Production rate of multi-fractured wells modeled with Gaussian pressure transients

Cited by 16 publications

References 28 publications

Production Forecasting of Unruly Geoenergy Extraction Wells Using Gaussian Decline Curve Analysis

Production Forecasting of Unruly Geoenergy Extraction Wells Using Gaussian Decline Curve Analysis

Probabilistic estimation of hydraulic fracture half-lengths: validating the Gaussian pressure-transient method with the traditional rate transient analysis-method (Wolfcamp case study)

Estimation of fracture half-length with fast Gaussian pressure transient and RTA methods: Wolfcamp shale formation case study

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