Managing the Cost of Enhanced Oil Recovery

Simandoux, P.; Champlon, D.; Valentin, E.

doi:10.2516/ogst:1990012

Cited by 8 publications

(6 citation statements)

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“…The pioneering empirical model for predicting saturation was the Archie [97] model for clean sandstone reservoirs. Several researchers have attempted to derive the relationship between water saturation and well log data to predict water saturation in different formations [98]- [102]. However, these approaches are constrained by their formation and are only applicable in restricted lithologies.…”

Section: Water Saturation Predictionmentioning

confidence: 99%

Machine Learning in Oil and Gas Exploration: A Review

Lawal,

Yang,

et al. 2024

IEEE Access

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A comprehensive assessment of machine learning applications is conducted to identify the developing trends for Artificial Intelligence (AI) applications in the oil and gas sector, specifically focusing on geological and geophysical exploration and reservoir characterization. Critical areas, such as seismic data processing, facies and lithofacies classification, and the prediction of essential petrophysical properties (e.g., porosity, permeability, and water saturation), are explored. Despite the vital role of these properties in resource assessment, accurate prediction remains challenging. This paper offers a detailed overview of machine learning's involvement in seismic data processing, facies classification, and reservoir property prediction. It highlights its potential to address various oil and gas exploration challenges, including predictive modelling, classification, and clustering tasks. Furthermore, the review identifies unique barriers hindering the widespread application of machine learning in the exploration, including uncertainties in subsurface parameters, scale discrepancies, and handling temporal and spatial data complexity. It proposes potential solutions, identifies practices contributing to achieving optimal accuracy, and outlines future research directions, providing a nuanced understanding of the field's dynamics. Adopting machine learning and robust data management methods is crucial for enhancing operational efficiency in an era marked by extensive data generation. While acknowledging the inherent limitations of these approaches, they surpass the constraints of traditional empirical and analytical methods, establishing themselves as versatile tools for addressing industrial challenges. This comprehensive review serves as an invaluable resource for researchers venturing into lesscharted territories in this evolving field, offering valuable insights and guidance for future research.INDEX TERMS Oil and gas exploration, machine learning, petrophysical properties prediction, facies and lithofacies classification, seismic data processing.

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Section: Water Saturation Predictionmentioning

confidence: 99%

Machine Learning in Oil and Gas Exploration: A Review

Lawal,

Yang,

et al. 2024

IEEE Access

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“…These models contain the Simandoux equation, the Indonesian equation, the Waxman-Smits model, the dual-water model, etc. [34][35][36][37][38][39]. These models are all raised to solve the water saturation calculation in shaly sands.…”

Section: Water Saturation Calculationmentioning

confidence: 99%

Formation Characterization and Type Prediction Based on Geophysical Well Log Data in Horizontal Well: A Case Study of Triassic Chang 8 Formation in Shunning Region, Central Ordos Basin

Li,

Xiao,

et al. 2023

Processes

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The role of the horizontal well in developing unconventional oil and gas reservoirs is particularly significant. Different from vertical wells, horizontal wells are greatly affected by many factors, e.g., well track, surrounding mudstone, resistivity, and pore structure heterogeneity in horizontal and vertical directions. These make it difficult to evaluate reservoir parameters and determine optimized test layers. In order to improve formation evaluation in horizontal wells, it is necessary to carry out the research of analyzing formation anisotropy, predict physical property parameters, and classify formation to determine high-quality intervals. In this study, taking Triassic Chang 8 Formation in Shunning Region, Central Ordos Basin as an example, 40 core samples were drilled and applied for nuclear magnetic resonance (NMR), mercury injection capillary pressure (MICP), and resistivity experiments. The porosity, permeability, resistivity, and pore structure anisotropy are analyzed. Results illustrate that the physical properties and pore structure in horizontal direction are superior to vertical direction. Meanwhile, NMR log loses its role in pore structure characterization in horizontal wells. Afterward, methods of correcting interval transit time (DT) and resistivity anisotropy are raised, and models of predicting formation physical property parameters, such as porosity, permeability, and water saturation, are established. A parameter, named as the formation type indicator (FTI), is proposed to reflect reservoir oil-bearing properties and pore structure. Finally, our target horizontal intervals are classified into four types, and the highest-quality “sweet spot” is determined.

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“…R w : formation water resistivity, ohm.m FT: formation temperature, ⁰F W S : water salinity, ppm Formation water saturation (S W ) was determined from R W that was corrected for shale and porosity using the following two equations, and the average value was implemented [25]: Simandoux [28]:…”

Section: Formation Water Resistivity From Sp Curve and Salinitymentioning

confidence: 99%

Case Study of Petrophysical Evaluation Utilizing Well Logs Data with Optimization of Reservoir Cut-off Parameters

Mahmud

Bennour

2020

Int. J. Petrol. Technol.

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Petrophysical evaluation of well log data is essential for the exploration and evaluation of hydrocarbon-bearing formations. Moreover, there are no standard criteria to implement cut-offs on petrophysical properties as a direction for economic decisions. In the present work, a petrophysical evaluation of well logging data from four wells in a mature oil field is performed to identify formation quality as a potential for hosting mature hydrocarbons reservoirs. Full consideration of cut-off values was taken into account. The cut-offs were estimated from well-recognized petrophysical relationships for permeability as a function of porosity, water saturation, and shale content. Results verification and calibration were also made based on laboratory measurements of petrophysical properties obtained from available core plugs in order to minimize uncertainty. Lithology analysis and characteristics revealed that the target formation is mainly sand and shale sequences. Results from well logs were in agreement with results obtained from core data. Formation effective porosity varies from 16 to 26% in all wells. A wide range of variations is observed in water saturation ranging from 28 to 57% and permeability ranging from 20 to 3300 mD. This is in good agreement with other measurements and well log analysis that show the mature formation remains to be a good hydrocarbon reservoir with significant potential.

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Managing the Cost of Enhanced Oil Recovery

Cited by 8 publications

References 0 publications

Machine Learning in Oil and Gas Exploration: A Review

Machine Learning in Oil and Gas Exploration: A Review

Formation Characterization and Type Prediction Based on Geophysical Well Log Data in Horizontal Well: A Case Study of Triassic Chang 8 Formation in Shunning Region, Central Ordos Basin

Case Study of Petrophysical Evaluation Utilizing Well Logs Data with Optimization of Reservoir Cut-off Parameters

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