Pore pressure prediction of some selected wells from the Southern Pletmos Basin, offshore South Africa

Ayodele, O.L.; Donker, Jan van Bever; Opuwari, Mimonitu

doi:10.2113/gssajg.119.1.203

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Also, numerous stacked gas-charged submarine sandstone fans defining a Kimmeridgian to Berriasian age reservoir were interconnected in the southern parts of the Gamtoos Basin. The offshore part of the Gamtoos Basin is located along with the other echelons of Outeniqua Basin, South Africa, with reservoir sand indicating a normal pressure with no overpressure zones (Ayodele et al 2016;Ayodele et al 2020).…”

Section: Geological Settingmentioning

confidence: 99%

Static reservoir modeling using stochastic method: a case study of the cretaceous sequence of Gamtoos Basin, Offshore, South Africa

Ayodele

Chatterjee

Opuwari

2021

J Petrol Explor Prod Technol

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Gamtoos Basin is an echelon sub-basin under the Outeniqua offshore Basin of South Africa. It is a complex rift-type basin with both onshore and offshore components and consists of relatively simple half-grabens bounded by a major fault to the northeast. This study is mainly focused on the evaluation of the reservoir heterogeneity of the Valanginian depositional sequence. The prime objective of this work is to generate a 3D static reservoir model for a better understanding of the spatial distribution of discrete and continuous reservoir properties (porosity, permeability, and water saturation). The methodology adopted in this work includes the integration of 2D seismic and well-log data. These data were used to construct 3D models of lithofacies, porosity, permeability, and water saturation through petrophysical analysis, upscaling, Sequential Indicator Simulation, and Sequential Gaussian Simulation algorithms, respectively. Results indicated that static reservoir modeling adequately captured reservoir geometry and spatial properties distribution. In this study, the static geocellular model delineates lithology into three facies: sandstone, silt, and shale. Petrophysical models were integrated with facies within the reservoir to identify the best location that has the potential to produce hydrocarbon. The statistical analysis model revealed sandstone is the best facies and that the porosity, permeability, and water saturation ranges between 8 and 22%, 0.1 mD (< 1.0 mD) to 1.0 mD, and 30–55%. Geocellular model results showed that the northwestern part of the Gamtoos Basin has the best petrophysical properties, followed by the central part of the Basin. Findings from this study have provided the information needed for further gas exploration, appraisal, and development programs in the Gamtoos Basin.

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Section: Geological Settingmentioning

confidence: 99%

Static reservoir modeling using stochastic method: a case study of the cretaceous sequence of Gamtoos Basin, Offshore, South Africa

Ayodele

Chatterjee

Opuwari

2021

J Petrol Explor Prod Technol

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“…Scholars such as Khattab used high-resolution full waveform inversion velocity from 1D seismic velocity profiles to 3D modeling to predict pre-drilling pore pressure 1 . Noah and others used interval seismic velocity to predict the pre-drilling pore pressure of land oilfields 2 , while Ayodele and colleagues used tomography generated by two-dimensional seismic data to extract grid maps to delineate pre-drilling overpressure and normal pressure formation pore pressure 3 . This method is regional, requiring consideration of multiple factors such as formation origin, rock type and structure, abrupt geological boundaries, and faults.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Liu

2023

Preprint

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The unpredictability of drilling pressure in bolt support systems has emerged as a significant constraint on support efficiency. Current research gaps exist in the field of machine learning for pre-drilling pressure prediction in bolt support and the selection method for key parameters (kernel function and historical points) in Gaussian processes. This study proposes a novel prediction method for bolt support drilling pressure, leveraging hybrid optimization algorithms to identify the key parameters in Gaussian process time series regression. Initially, the Gaussian process time series regression algorithm is modeled. Through data computation and simulation, it is observed that employing the Gaussian process time series algorithm for predicting the drilling pressure of bolt support results in substantial variation in the outcomes when different combinations of kernel functions and historical points are used. Therefore, it is essential to identify the optimal kernel function and the most suitable number of historical points before utilizing the Gaussian process time series algorithm for predicting drilling pressure. Subsequently, three hybrid optimization algorithms (GA-GPR, PSO-GPR, and ACA-GPR) are employed to iteratively optimize the two key parameters (kernel function and historical points) in Gaussian process time series regression. Among these, the PSO-GPR algorithm proves to be the most effective for identifying the kernel function and historical points of the key parameters in the Gaussian process time series algorithm when applied to the prediction of drilling pressure in bolt support. Remarkably, even with a small sample size and a limited number of iterations, PSO-GPR achieves 80% accuracy while reducing time consumption by 60%. Finally, a prediction system for drilling pressure in underground bolt support is established. The algorithm's generalization capability is verified through the prediction of actual drilling pressure. Thus, this study provides a robust and efficient method for predicting drilling pressure in bolt support systems, potentially enhancing support efficiency significantly.

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“…To understand the storage and flow capacities of and the petrophysical behaviour of the FU, HFU and PRT within a reservoir, the Stratigraphy-Modified Lorenz Plot (SMLP) is one of the methods that has proven to be successfully applied by many authors in both carbonate and clastic reservoirs [14]. The impact of the depositional environment and diagenesis on reservoir quality is crucial because of the heterogeneity of sedimentary rocks, which also contributes to the variations of reservoir quality within a reservoir [15][16][17][18][19][20]. The quality of a reservoir is the primary factor that controls the prospectivity, especially during the exploration and production of hydrocarbon [21,22].…”

Section: Introductionmentioning

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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Pore pressure prediction of some selected wells from the Southern Pletmos Basin, offshore South Africa

Cited by 6 publications

References 5 publications

Static reservoir modeling using stochastic method: a case study of the cretaceous sequence of Gamtoos Basin, Offshore, South Africa

Static reservoir modeling using stochastic method: a case study of the cretaceous sequence of Gamtoos Basin, Offshore, South Africa

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

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

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