Development of a Machine Learning Assisted Framework in Calibrating In-situ Stress Changes Through Time-lapse Vertical Seismic Profiling

Ampomah, William; Acheampong, Samuel; McMillan, Marcia; Bratton, Tom; El-Kaseeh, George

doi:10.2139/ssrn.4285741

Cited by 4 publications

(2 citation statements)

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“…FWU estimated an HIIP of 120 MM stb, and initial gas in place of 41.48 BSCF. Initial reservoir pressure and bubble point pressure as well as minimum miscible pressure (MMP) of carbon dioxide and the crude oil were estimated to be 2217.7 psia, 2073.7 psia and 4200 psia, respectively [47]. At an initial reservoir temperature of 168 o F, the formation volume factor was 1.192 rb/stb, with a solution gas drive as the primary drive mechanism since there was no associated aquifer.…”

Section: Reservoir Engineering Datamentioning

confidence: 99%

Mechanisms of Waterflood Inefficiency: Analysis of Geological, Petrophysical and Reservoir History, a Field Case Study of FWU (East Section)

Morgan,

Ampomah,

Grigg

et al. 2024

Energies

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The petroleum reservoir represents a complex heterogeneous system that requires thorough characterization prior to the implementation of any incremental recovery technique. One of the most commonly utilized and successful secondary recovery techniques is waterflooding. However, a lack of sufficient investigation into the inherent behavior and characteristics of the reservoir formation in situ can result in failure or suboptimal performance of waterflood operations. Therefore, a comprehensive understanding of the geological history, static and dynamic reservoir characteristics, and petrophysical data is essential for analyzing the mechanisms and causes of waterflood inefficiency and failure. In this study, waterflood inefficiency was observed in the Morrow B reservoir located in the Farnsworth Unit, situated in the northwestern shelf of the Anadarko Basin, Texas. To assess the potential mechanisms behind the inefficiency of waterflooding in the east half, geological, petrophysical, and reservoir engineering data, along with historical information, were integrated, reviewed, and analyzed. The integration and analysis of these datasets revealed that several factors contributed to the waterflood inefficiency. Firstly, the presence of abundant dispersed authigenic clays within the reservoir, worsened by low reservoir quality and high heterogeneity, led to unfavorable conditions for waterflood operations. The use of freshwater for flooding exacerbated the adverse effects of sensitive and migratory clays, further hampering the effectiveness of the waterflood. In addition to these factors, several reservoir engineering issues played a significant role in the inefficiency of waterflooding. These issues included inadequate perforation strategies due to the absence of detailed hydraulic flow units (HFUs) and rock typing, random placement of injectors, and uncontrolled injected fresh water. These external controlling parameters further contributed to the overall inefficiencies observed during waterflood operations in the east half of the reservoir. A detailed understanding of the mechanistic factors of inefficient waterflood operation will provide adequate insights into the development of the improved recovery technique for the field.

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Section: Reservoir Engineering Datamentioning

confidence: 99%

Mechanisms of Waterflood Inefficiency: Analysis of Geological, Petrophysical and Reservoir History, a Field Case Study of FWU (East Section)

Morgan,

Ampomah,

Grigg

et al. 2024

Energies

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“…The Southwest Regional Partnership on Carbon Sequestration (SWP) has conducted several studies on FWU in the context of CO 2 -EOR operations, as evidenced by the works of Rasmussen et al 43 , Acheampong 44 , Will et al 45 , Adu-Gyamfi et al 46 , Ampomah et al 47 and Acheampong et al 4,5 The SWP is a constituent member of a larger network of seven partnerships that are engaged in a nationwide endeavor to investigate the sequestration and permanent containment of carbon dioxide as a means of mitigating the effects of global warming. SWP studies are geared towards assuring CO 2 containment within the FWU storage compartment.…”

Section: Background Studymentioning

confidence: 99%

Time‐lapse VSP integration and calibration of subsurface stress field utilizing machine learning approaches: A case study of the morrow B formation, FWU

et al. 2023

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This study aims to develop a methodology for calibrating subsurface stress changes through time‐lapse vertical seismic profiling (VSP) integration. The selected study site is a region around the injector well located within Farnsworth field unit (FWU), where there is an ongoing CO2‐enhanced oil recovery (EOR) operation. In our study, a site‐specific rock physics model was created from extensive geological, geophysical, and geomechanical characterization through 3D seismic data, well logs, and core assessed as part of the 1D MEM conducted on the characterization well within the study area. The Biot‐Gassmann workflow was utilized to combine the rock physics and reservoir simulation outputs to determine the seismic velocity change due to fluid substitution. Modeled seismic velocities attributed to mean effective stress were determined from the geomechanical simulation outputs, and the stress‐velocity relationship developed from ultrasonic seismic velocity measurements. A machine learning‐assisted workflow comprised of an artificial neural network and a particle swarm optimizer (PSO) was utilized to minimize a penalty function created between the modeled seismic velocities and the observed time‐lapse VSP dataset. The successful execution of this workflow has affirmed the suitability of acoustic time‐lapse measurements for 4D‐VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

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Improving Subsurface Characterization Utilizing Machine Learning Techniques

Koray

Bui

Ampomah

et al. 2023

Day 2 Tue, May 23, 2023

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The objective of this paper is to present a framework that applies machine learning to reservoir characterization. Machine learning applications in the oil and gas industry is rapidly becoming popular and in recent years has been utilized for the characterization of various reservoirs. Conventional reservoir characterization employs core data measurements and local correlations between porosity and permeability as input data for reservoir property modeling. However, a strong correlation between porosity and permeability as well as reliable core measurements are not always available. The proposed approach uses both well logs and core data to construct different models to predict permeability using three distinct methods including a parametric, non-parametric, and machine learning technique. The parametric method employed the known relationship between porosity and the natural log of permeability. The non-parametric regression method utilized the alternating conditional expectation (ACE) algorithm. The third approach involved machine learning workflow implemented within a commercial software. The reservoir was first classified into distinct hydraulic flow units using the flow zone indicator (FZI) approach and k-means clustering. Permeability was then predicted using a supervised machine-learning framework. A field case study was then utilized to ascertain the effectiveness of these approaches by validating the model with data from one of the wells. The results of these three approaches were compared using the mean absolute error (MAE) and mean squared error (MSE) values in the validation process. An examination of the error calculated found the support vector machine (SVM) and linear regression algorithms in characterizing the upper reservoir region and the SVM for the lower reservoir characterization yielding the best results when using the machine learning approach thus, yielding the least error as compared to the other two approaches. Additional validation was performed by comparing different models based on permeability fields through numerical model calibration to historical data. It was found that machine learning-based permeability had the least error compared to calibration data prior to the history matching process. The investigated reservoir consists of two distinct productive oil zones separated by an impermeable shale. There are 15 existing wells that have been producing from both the upper and lower zones since 1997. Using machine learning permeability-based model, the history matching process was conducted successfully to match both observed production data and pressure data of 15 wells with less than 10% global deviation. This study presents the feasibility of applying several different approaches in predicting permeability based on gamma ray, bulk density, and deep resistivity logs. The machine learning approach proves its high potential and readiness in supporting reservoir characterization and history matching compared to the other approaches.

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Development of a Machine Learning Assisted Framework in Calibrating In-situ Stress Changes Through Time-lapse Vertical Seismic Profiling

Cited by 4 publications

References 0 publications

Mechanisms of Waterflood Inefficiency: Analysis of Geological, Petrophysical and Reservoir History, a Field Case Study of FWU (East Section)

Mechanisms of Waterflood Inefficiency: Analysis of Geological, Petrophysical and Reservoir History, a Field Case Study of FWU (East Section)

Time‐lapse VSP integration and calibration of subsurface stress field utilizing machine learning approaches: A case study of the morrow B formation, FWU

Improving Subsurface Characterization Utilizing Machine Learning Techniques

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