Enhanced hydrocarbon recovery using the application of seismic attributes in fault detection and direct hydrocarbon indicator in Tomboy Field, western-Offshore Niger Delta Basin

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.

Section: Importance Of Reservoir Characterizationmentioning

confidence: 99%

“…The essential instrument for subsurface imaging is seismic data analysis, which offers details about the lithology, fluid distribution, and subsurface structures [27,8], Fig. 4.…”

Section: Seismic Surveymentioning

confidence: 99%

Reservoir Characterization Review in Sedimentary Basins

Ibekwe

Oguadinma²,

Okoro³

et al. 2023

“…These emissions contribute to the formation of ground-level ozone, a key component of smog, and have detrimental effects on air quality and respiratory health (Jones & Brown, 2019). Methane, a potent greenhouse gas, is also released during the extraction and flaring of natural gas, exacerbating climate change [3,4,16].…”

Section: Air Pollution By Oil and Gas Exploitationmentioning

confidence: 99%

“…The processing and refining of oil and gas contribute to air pollution. Refineries emit pollutants such as sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) during the refining process [16]. These emissions have been associated with respiratory and cardiovascular health issues.…”

Section: Air Pollution By Oil and Gas Exploitationmentioning

confidence: 99%

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

Ukachukwu,

Uzoamaka,

Elochukwu

2023

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.

The Application of Seismic Attributes in Fault Detection and Direct Hydrocarbon Indicator in Tomboy Field, Western-Offshore Niger Delta Basin

Ibekwe

Arukwe

Ahaneku

et al. 2023

Seismic attribute analysis is important in subsurface data interpretation, such as seismic interpretation, which could involve seismic stratigraphic and structural interpretation. This interpretation is often hampered by seismic resolution and, sometimes, human inability to identify a subtle feature on the seismic. These factors have frequently led to the poor seismic interpretation of geologic features. Thus, an integral approach to studying structural patterns and hydrocarbon bearing zones using seismic attributes was carried out on the Tomboy field using 3D seismic data covering approximately 56 km2 of the western belt of the Niger Delta. The seismic volume underwent post-stack processing, which enhanced seismic discontinuities. A deep steering volume was first created, and several dip filters were applied to enhance faults in the study area. After that, curvature and similarity attributes were calculated on the dip-steered and fault-enhanced volume. These calculations show detailed geometry of the faults and zones of subtle lineaments. Six faults (F1, F2, F3, F4, F5 and F6) were identified and mapped. These faults range from antithetic to crest growth faults. Two major growth faults (F5 and F6) were revealed to dip in the northeast to southwest directions. A near-extensive crest fault (F4) appeared beneath the major faults. Although several minor fractures were displayed in the southern and central portions of the crest fault of the dipping seismic data, the southwest (F4) and growth fault, F6, are responsible for holding the hydrocarbon found within the identified closures. Using attributes on the seismic data increased confidence in mapping and interpreting structural features. Furthermore, energy attributes were used as Direct Hydrocarbon Indicators (DHI) to visualize viable areas within the study, which allows a more robust interpretation. Time slices were taken in regions of flat and bright spots. The spectral decomposition attribute was run on these slices to display areas of high amplitude reflection typical of hydrocarbon-bearing regions trapped mainly by regional to sub-regional growth faults. The surface attribute calculated on the generated surface shows that the field is predominantly controlled by faults serving as traps for hydrocarbon.