Hydrocarbon Reservoir Evaluation: a case study of Tymot field at southwestern offshore Niger Delta Oil Province, Nigeria

Nwaezeapu, Victor Cypren; Tom, Izediunor U.; David, Ede T. A.; Vivian, Oguadinma O

doi:10.18063/nn.v0i0.618

Cited by 4 publications

(6 citation statements)

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“…Non-uniqueness can cause predictions of reservoir behaviour to be imprecise since different models may yield inconsistent results. To validate their projections, reservoir engineers must carefully assess the model's underlying assumptions and unknowns [38][39][40][41][42].…”

Section: Non-uniquenessmentioning

confidence: 99%

Reservoir Characterization Review in Sedimentary Basins

Ibekwe

Oguadinma²,

Okoro³

et al. 2023

JENRR

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Evaluating the subsurface characteristics of reservoirs is an important part of gas storage, hydrocarbon exploration, and production in sedimentary basins. This process combines geological, geophysical, and engineering data to understand the subsurface geology, and fluid distribution, determine reserves and predict the fluid movement in the reservoir. And in the case of empty reservoir, the storage capacity, sealing strength is analysed. The primary goal of reservoir characterization is to create a precise and dependable reservoir model to maximize the production procedure and reduce the associated risks of hydrocarbon exploration and production. This review examines different approaches used for reservoir characterization in sedimentary basins, including geological, geophysical, and engineering methods. Each method's advantages and disadvantages are discussed, alongside their uses in different reservoir contexts. The importance of combining multiple lines of evidence to enhance the accuracy of the reservoir models is also examined.

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Section: Non-uniquenessmentioning

confidence: 99%

Reservoir Characterization Review in Sedimentary Basins

Ibekwe

Oguadinma²,

Okoro³

et al. 2023

JENRR

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“…Air pollution resulting from oil and gas reservoir exploitation [4,[11][12][13][14]] is a significant environmental concern. Throughout the various stages of oil and gas production, including extraction, processing, and transportation, a wide range of pollutants are released into the atmosphere, posing risks to human health and the environment.…”

Section: Air Pollution By Oil and Gas Exploitationmentioning

confidence: 99%

“…Specifically, machine learning has been applied in pollution investigation, mitigation, and control. For instance, machine learning is used in water pollution to develop a competent and robust approach to estimating water quality characteristics [2][3][4][5]. In addition, recent trends and advances in computer-aided environmental data engineering have also facilitated the adoption of machine learning in environmental pollution management.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Air Pollution Studies: A Systematic Review

Ukachukwu,

Uzoamaka,

Elochukwu

2023

JENRR

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Air pollution is a serious global issue that threatens human life and health, as well as the environment. Machine learning algorithms can be used to predict air pollution level data from both natural and anthropogenic activities. Environmental and government agencies can use these speculations to issue air pollution alerts. This review work is an attempt at the recent status and development of scientific studies on the use of machine learning algorithms to model air pollution challenges. This study uses the scientific web as a primary search engine and covers over 100 highly peer-reviewed articles from 2000-2022. Therefore, this review paper aims to highlight the various application methods of machine learning, notably data mining, in air pollution control and monitoring. It also comprehensively analyses published works by renowned scholars and authors worldwide, discussing how machine learning has been used in mitigating air pollution. By examining the chronological trends of machine learning in air pollution, this review paper provides an up-to-date account of the successes achieved in regulating air pollution using machine learning techniques. Additionally, it identifies areas that require further research, critically analyzing the current state of knowledge and potential research directions.

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“…Also, the use of seismic attributes has proven to be one of the best techniques for quantitative seismic interpretation, as the method can validate hydrocarbon anomalies and give valuable information during prospect evaluation, reservoir characterization, and production simulation (Taner et al, 1979;Schlumberger 2007a;Oguadinma et al 2016;Nwaezeapu et al, 2018). The amplitude and frequency responses of the reflected seismic wave are influenced by various factors, including geologic structure, layer thickness, lithology, and pore fluid properties (Taner et al, 1979).…”

Section: 0: Introductionmentioning

confidence: 99%

Reservoir and sequence stratigraphic analysis using subsurface data

Vivian

Kelechi

Lanisa

et al. 2023

Preprint

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There is a growing interest in the Niger Delta hydrocarbon field exploration and exploitation. With growing interest in proven field prospects and confirmed reservoir facies, there is a need for improved basin and field scale reservoir identification, analysis, and assessment. The Agbada Formation of the middle Miocene age across Vim Field, in the western part of the coastal swamp depobelt was studied using wire-line logs, 3D seismic and biostratigraphic data. This contribution evaluated stratigraphic configuration, structural style, facies geometry, hydrocarbon leads, and distribution. The method adopted involves delineating lithologies from the gamma-ray log, identifying reservoirs from the resistivity and gamma ray logs, well top correlation using gamma ray logs, horizon and fault mapping, time to depth conversion of maps using velocity model, attribute analysis, and sequence stratigraphic analysis. Bounding surfaces were mapped on the seismic section, and well correlations were conducted and integrated with a paleobathymetry chart to highlight the depositional environments. Eight major stratigraphic bounding surfaces (four sequence boundaries and four maximum flooding surfaces) were identified. Erosional bounding surfaces were mapped as interest surface horizons. Analysis of the vertical succession of depositional facies revealed five 3rd order depositional sequences of Mid-Miocene age-bound SB and MFS chronologically. Three strata stacking patterns (progradational, retrogradation, and aggradational) were delineated and interpreted as lowstand systems tract (LST), highstand systems tract (HST), and transgressive systems tract (TST). The alternation of the reservoir sands of the HST and TST and the shale units of the TST offers good traps for hydrocarbon. Fault analysis revealed regional growth faults, counter-regional growth faults, synthetic and antithetic faults that show foot walls and hanging walls dominating the mainly extensional zone. These faults play a major role in hydrocarbon trapping within the area. The depositional environment spans from shelf to slope margin, with sediments deposited within the Inner neritic through the middle neritic to the outer neritic settings. Interpretative observation shows that thin-bedded shale facies complexly intercalate the reservoir facies of the TST. Thus careful evaluation of such reservoirs is recommended before making exploitation decisions.

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Hydrocarbon Reservoir Evaluation: a case study of Tymot field at southwestern offshore Niger Delta Oil Province, Nigeria

Cited by 4 publications

References 0 publications

Reservoir Characterization Review in Sedimentary Basins

Reservoir Characterization Review in Sedimentary Basins

Application of Machine Learning to Air Pollution Studies: A Systematic Review

Reservoir and sequence stratigraphic analysis using subsurface data

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