Figure 7 - available via license: Creative Commons Attribution-NonCommercial 4.0 International
Content may be subject to copyright.
Source publication
Aim: This study presents the log analysis results of a log suite comprising gamma ray (GR), resistivity (LLD), neutron (PHIN), density (RHOB) logs and a 3D seismic interpretation of Tymot field located in the southwestern offshore of Niger delta. This study focuses essentially on reserves estimation of hydrocarbon bearing sands. Well data were used...
Context in source publication
Similar publications
This study presents the log analysis results of a log suite comprising gamma ray (GR), resistivity (LLD), neutron (PHIN), density (RHOB) logs and a 3D seismic interpretation of TIM field located in the southwestern offshore of Niger Delta, Nigeria. This study focuses essentially on reserves evaluation of hydrocarbon-bearing sands. Well data were us...
Citations
... Flow processes and the Corresponding author: thankgod.ujowundu@gmail.com relationship to channel-belt aggradation were approached starting with the use of 3D reflection Nwaezeapu et al., 2018;Oguadinma et al., 2016;Oguadinma et al., 2017;Aniwetalu et al., 2018;, while Abd-ElGawad et al. (2012) used a 3-d numerical model to simulate turbiditic current. ...
This study explores geomorphology within canyons using 3D seismic imaging. It reveals crucial insights that underscore the relationship between growth faults, slope instability, and canyon development. The structural settings of canyons, often conforming to established patterns, come to light as integral components of the study. An important observation was made: the gradient, a first-order factor, exerts significant control over both canyon initiation and propagation. Furthermore, the distribution of growth faults, a major catalyst for slope instability, holds an indirect yet profound link to sediment routing within canyons and the ancient geography of the region. These findings not only enhance our understanding of canyon evolution but also play a crucial role in interpreting reservoir distribution, marking them as invaluable assets in some geological exploration and reservoir management.
... mechanical strength, porosity, and permeability. These properties are essential for understanding the behavior of the reservoir, identifying the optimal production zones, and designing the appropriate drilling and completion strategies.(Nwaezeapu et al., 2018;Aniwetalu et al., 2018;Li, et al., 2019) ...
Geomechanics plays an essential role in the drilling and production of petroleum reservoirs beginning with
geomechanical models to its applications in petroleum reservoirs. Major current applications in this domain
include the characterization of rock properties, such as porosity, permeability, and mechanical strength. One
important application of geomechanics in petroleum reservoirs is in hydraulic fracturing, a technique used to
stimulatetheproductionofoilandgasfromunconventionalreservoirssuchasshaleformations.Itcanalso
pre-drill well planning and continuing with wellbore stability support while drilling and sand production
beappliedinpetroleumreservoirsinwellborestabilityanalysis.Asoilandgaswellsaredrilleddeeperand
management. This paper will cover an overview of geomechanics from its fundamental knowledge and
in more complex formations, the stability of the wellbore becomes increasingly important.
Keywords: Geomechanics, Reservoir, Hydraulic fracturing, Permeability, wellbore stability.
... The artificial neural networks (ANNs) model is used to estimate the oil flow rate as a function of the following parameters: choke upstream pressure, choke size, and the producing gas-to-oil ratio [16]. [16] stated that most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans [7,[17][18][19][20][21][22]. However, this process costs an immense number of resources and is time-consuming [23]. ...
... The first to make pore pressure predictions from shale properties derived from well-log data, such as acoustic travel time/velocity and resistivity, were identified in [25]. [19] and [22] also use similar methods for reservoir optimization. They contended that porosity or transit time in shale is abnormally high relative to its depth if the fluid pressure is abnormally high, and later analyzed the data presented by [25]. ...
Accurately predicting pore pressure and optimizing reservoirs in the oil and gas industry is crucial for the exploration and production of hydrocarbon reservoirs. Traditional geophysical methods of pore pressure prediction and reservoir optimization require extensive manual effort and may not fully utilize available data. However, in order to surmount these constraints, deep learning has revolutionized these procedures by engaging in intricate pattern recognition, feature extraction, and predictive modelling. Deep learning models such as Artificial neural network, convolution neural network, Pore-net, FCN, DeepLab V3 +, LSTM, and BP can capture complex patterns those traditional methods might miss. Despite a lack of recorded information in wells, deep learning has significantly reduce uncertainty in pore pressure prediction when information is insufficient. In pore pressure prediction and reservoir optimization, deep learning models can analyse a vast amount of seismic, well log, and geological data to accurately predict pore pressure distribution in subsurface formations and can assist in making informed decisions about production strategies. This helps maximize hydrocarbon recovery, minimize operational costs, and extend the productive life of the reservoir, with better-informed choices, reduced uncertainties, and optimized hydrocarbon recovery from subsurface reservoirs, geoscientists and reservoir engineers can make confident decisions that positively impact the industry. Despite ongoing obstacles such as scarcity of data in developing countries and the complexity of predicting unconventional formations, it is indisputable that utilizing deep learning offers significant advantages. Further research and integration of deep learning with other technologies is recommended in order to facilitate the creation of more efficient approaches for predicting pore pressure and optimizing reservoirs.
... This environmental degradation can result in the loss of useful land that is utilized for farming, domestic, industrial, and other purposes. Additionally, it can cause damage to properties and even lead to loss of life [1,2,3]. According to [4], Anambra State in the Southeastern region of Nigeria has the highest number of active gullies, with many of them having failed gully-control measures. ...
In South-Eastern Nigeria, particularly in Anambra State, gully erosion presents a serious environmental challenge. With over 100 gully sites in the state, only about 30 have received measures of control but are still not fully under control. Observations have shown clearly that the underlying geology exerts major control over the development of gully erosion in the study area. Progressive gullies in certain areas of Anambra State are caused by various factors, including topography, soil and water pH, lithology type, deforestation, hydrogeology, and geotechnical rock properties. This erosion activity has resulted in the loss of productive lands, water pollution, sedimentation of waterways, and the loss of lives and properties almost every year. However, several government agencies have attempted to manage it using concrete structures, stabilization work such as planting bamboo and cashew trees to increase water intake, and pipe structures to channel the water directly to nearby surface waters through the construction of check dams, embankments, and retention ponds to control the flow of water and sediments. Despite these control efforts, several challenges persist in effectively managing gully erosion in Anambra State. Majority of the concrete structures used to control these gullies have collapsed, leading to the incessant spreading of the site. Inadequate funding, failure of engineering structures, flooding, the geologic setting of the area, limited technical expertise, population growth, urbanisation, a lack of public awareness, and a lack of proper coordination among stakeholders hinder the implementation of comprehensive erosion control measures. It is recommended that concrete drainage channels should not only be used in controlling these gullies but also be integrated with other measures to yield a positive result.
... Since more than 50 attributes of seismic data have been obtained, more attributes have been found with advanced computer technology. For reservoir characterisation [5][6][7][8] and seismic interpretations [9], (Oguadinma et al. 2018), seismic attributes analysis has been used by many authors [10], (Strecker et al. 2004); [11][12][13]. It also proved its importance in the classification of the depositional environment [14,15,16] and in the detection and improvement of fractures and faults [17][18][19][20] to give details of structural history and provide direct hydrocarbon indicator [21]. ...
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.
... Characterizing reservoirs (Mahmood and Vaziri-Moghaddam, 2016;Chen, et al., 2019) is a vital step that combines various types of information to create a dependable model of the reservoir (Gupta and Kumar, 2020;Cao, et al., 2023;Aksel, et al., 2023). This model encompasses the geometry, composition, porosity, permeability, fluid characteristics, and fluid positioning in the reservoir (Luo et al., 2018;Nwaezeapu, et al., 2018;Zhang, 2021). To grasp the rock properties, fluid distribution, and flow capability of subsurface reservoirs, geological, geophysical, and engineering details must be integrated (Luo et al., 2018). ...
Evaluating the subsurface characteristics of reservoirs is an important part of hydrocarbon exploration and production in sedimentary basins. This process combines geological, geophysical, and engineering data to comprehend the subsurface geology and fluid distribution, determine reserves, and predict the fluid movement in the reservoir. The primary goal of reservoir characterization is to create a precise and dependable model of the reservoir to maximize the production procedure and reduce the associated risks of hydrocarbon exploration and production. This review examines different approach utilized 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.
... 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). ...
In petroleum geology, machine learning (ML) has shown promise as a method for improving exploration and production. Current uses of ML algorithms in this area have significantly improved data analysis, modeling, and prediction, allowing for better decision-making and cost reductions. ML is utilized for a number of activities, including well-log interpretation, reservoir characterization, seismic data processing, drilling optimization, and production forecasting. The interpretation of well logs is one of the main uses of ML in petroleum geology. Using well logs, ML algorithms can categorize different types of rocks, identify lithology, and calculate porosity and permeability. In order to predict fluid distribution, identify the existence of hydrocarbons, and calculate reservoir parameters like thickness and depth, ML models are also employed for reservoir characterization. To better characterize reservoirs and make well placement decisions, ML algorithms are employed in seismic data processing to locate faults, fractures, and other geological features. ML models have been applied in conventional oil fields and unconventional shale plays in places like the Permian Basin in the United States and the North Sea in Europe. It has also being used in complex geological settings, such as tight gas deposits in the Marcellus Shale and fractured carbonate reserves in the Middle East. By forecasting drilling performance, spotting abnormalities in drilling data, and choosing the most effective drilling parameters, ML is also used to optimize drilling operations. The optimum production techniques are found, the remaining recoverable reserves are estimated, and production rates are predicted using machine learning (ML) in production forecasting. Cost-saving manufacturing optimization is possible with the help of this information. Overall, recent ML applications in petroleum geology have yielded encouraging results, allowing for better decision-making, cost reduction, and increased productivity. The oil and gas sector is expected to see ML approaches play a more significant role as they advance.
