Hydraulic unit classification of un-cored intervals/wells and its influence on the productivity performance

Yu, Pao Shan

doi:10.1016/j.petrol.2020.107980

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…This study employs the Reservoir Quality Index (RQI) as a static reservoir attribute to measure the distance between pairs of realizations. RQI finds widespread applications in various reservoir-related domains, including permeability estimation, stratigraphy, and reservoir modeling [36][37][38][39][40]. Porosity and permeability, integral petrophysical properties within the RQI formulation, hold particular significance in the context of fluid flow modeling in heterogeneous porous media [41,42], especially in the modeling of CO 2 storage and plume migration [43,44].…”

Section: Euclidean Distance Measurement Between Realizationsmentioning

confidence: 99%

Uncertainty Quantification in CO2 Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Model Using Representative Geological Realizations and Unsupervised Machine Learning

Mahjour,

Badhan,

Faroughi

2024

Energies

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Evaluating uncertainty in CO2 injection projections often requires numerous high-resolution geological realizations (GRs) which, although effective, are computationally demanding. This study proposes the use of representative geological realizations (RGRs) as an efficient approach to capture the uncertainty range of the full set while reducing computational costs. A predetermined number of RGRs is selected using an integrated unsupervised machine learning (UML) framework, which includes Euclidean distance measurement, multidimensional scaling (MDS), and a deterministic K-means (DK-means) clustering algorithm. In the context of the intricate 3D aquifer CO2 storage model, PUNQ-S3, these algorithms are utilized. The UML methodology selects five RGRs from a pool of 25 possibilities (20% of the total), taking into account the reservoir quality index (RQI) as a static parameter of the reservoir. To determine the credibility of these RGRs, their simulation results are scrutinized through the application of the Kolmogorov–Smirnov (KS) test, which analyzes the distribution of the output. In this assessment, 40 CO2 injection wells cover the entire reservoir alongside the full set. The end-point simulation results indicate that the CO2 structural, residual, and solubility trapping within the RGRs and full set follow the same distribution. Simulating five RGRs alongside the full set of 25 GRs over 200 years, involving 10 years of CO2 injection, reveals consistently similar trapping distribution patterns, with an average value of Dmax of 0.21 remaining lower than Dcritical (0.66). Using this methodology, computational expenses related to scenario testing and development planning for CO2 storage reservoirs in the presence of geological uncertainties can be substantially reduced.

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Section: Euclidean Distance Measurement Between Realizationsmentioning

confidence: 99%

Uncertainty Quantification in CO2 Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Model Using Representative Geological Realizations and Unsupervised Machine Learning

Mahjour,

Badhan,

Faroughi

2024

Energies

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“…The results produced the cumulative storage capacity (porosity multiplied by the bed thickness) and cumulative flow capacity (permeability multiplied by bed thickness). The cumulative flow capacity is plotted on the vertical axis against the cumulative storage capacity on the horizontal axis to produce a graph used to estimate storage and flow capacities [43,50,51]. Line segments and slopes of the graph separated by an inflexion point are used to represent flow and storage capacities.…”

Section: Smlpmentioning

confidence: 99%

The Impact of Detrital Minerals on Reservoir Flow Zones in the Northeastern Bredasdorp Basin, South Africa, Using Core Data

et al. 2022

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The present study uses core data to group reservoirs of a gas field in the Bredasdorp Basin offshore South Africa into flow zones. One hundred and sixty-eight core porosity and permeability data were used to establish reservoir zones from the flow zone indicator (FZI) and Winland’s methods. Storage and flow capacities were determined from the stratigraphy-modified Lorenz plot (SMLP) method. The effects of the mineralogy on the flow zones were established from mineralogy composition analyses using quantitative X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM). Results reveal five flow zones grouped as high, moderate, low, very low, and tight reservoir rocks. The high flow zone is the best reservoir quality rock and has porosity and permeability values ranging from 12 to 20% and 100 to 1000 mD. The high and moderate zones contribute more than 60% of each well’s flow capacities. The moderate and low flow zone extends laterally to all the wells. The tight flow zone is an impervious rock and has the lowest rock quality with porosity and permeability values less than 8% and 1 mD, respectively. This zone contributes less than 1% to flow capacity. The impact of minerals on flow zones is evident in plagioclase and muscovite content increases. An accompanied decrease in quartz content is observed, which implies that low plagioclase content ≤4% and muscovite content of ≤1% corresponds to the low, moderate, and high flow zones, while plagioclase content of ≥4% and muscovite content of ≥1% belong to the tight flow zone. Consequently, the quantity of plagioclase and muscovite can be used as a proxy to identify better quality reservoir rocks. The diagenetic process that reduces the rock quality can be attributed to quartz overgrowth and the accumulation of mica flakes in the pore spaces. In contrast, the fracture in the high flow zone is the reservoir quality enhancing process. The flow zones are generally controlled by a combination of facies and diagenetic factors.

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Study on a Prediction Method of Hydraulic Units Using Logging Curves Constrained by Complex Lithofacies: A Case Study of the Tidal Estuarine Reservoir in Napo Formation, Oriente Basin

Zhu,

Sun,

Zhang

et al. 2024

Springer Series in Geomechanics and Geoengineering

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Hydraulic unit classification of un-cored intervals/wells and its influence on the productivity performance

Cited by 7 publications

References 19 publications

Uncertainty Quantification in CO2 Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Model Using Representative Geological Realizations and Unsupervised Machine Learning

Uncertainty Quantification in CO2 Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Model Using Representative Geological Realizations and Unsupervised Machine Learning

The Impact of Detrital Minerals on Reservoir Flow Zones in the Northeastern Bredasdorp Basin, South Africa, Using Core Data

Study on a Prediction Method of Hydraulic Units Using Logging Curves Constrained by Complex Lithofacies: A Case Study of the Tidal Estuarine Reservoir in Napo Formation, Oriente Basin

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