Cross plot Analysis of Rock Properties from Well Log Data for gas detection in Soku Field, Coastal Swamp Depobelt, Niger Delta Basin

The cross plotting of rock properties for fluid and lithology discrimination was carried out in a Niger Delta oil field using well data X-26 from a given oil field in the coastal swamp depobelt. The data used for the analysis consisted of suites of logs, including gamma ray, resistivity, sonic and density logs only. The reservoir of interest Horizon 1, was identified using the available suite of logs on the interval where we have low gamma ray, high resistivity and low acoustic impedance specifically at depths 10,424ft (3177.24m) to 10 724ft (3268m). We first obtained other rock attributes from the available logs before cross plotting. The inverse of the interval transit times of the sonic logs were used to generate the compressional velocities and the S-wave data was generated from Castagna´s relation. Employing rock physics algorithm on Hampson Russell software (HRS), rock attributes including Vp/Vs ratio, Lambda-Rho and Mu-Rho were also extracted from the well data. Cross plotting was carried out and Lambda Rho (λρ) versus MuRho (μρ) crossplots proved to be more robust for lithology identification than Vp versus Vs crossplots, while λρ Versus Poisson impedance was more robust than Vp/Vs versus Acoustic impedance for fluid discrimination, as well as identification of gas sands. The crossplots were consistent with Rock Physics Templates (RPTs). This implies the possibility of further using the technique on data points of inverted sections of various AVO attributes within the field in areas not penetrated by wells within the area covered by the seismic

SEISMIC ANALYSIS OF THE TRANSGRESSIVE SYSTEMS TRACTS (TSTS) OF THE NIGER DELTA

One way of identifying our MFSs is to look out for shale tops of high acoustic properties within a shale interval that corresponds to the lowest resistivity values and widest separation between neutron and density values. The TSTs culminate to a MFS as it comprises the deposits accumulated from the onset of coastal transgression until the time of maximum transgression of the coast, just prior to renewed regression (SepmStrata, 20). The seismic character of the shales within these TSTs could vary factoring the effect of depth trends, hence a need to understand the trend with increasing depth and thereby increased compaction. From generated synthetic, using the seismic responses at interfaces within the lithologies cut across by one of our HP well in the Central Swamp depobelt, a study integrating Reflectivity Pattern Analysis (RPA) and Sequence Stratigraphic analysis was carried out to understand the behavior of our shales within the TSTs. Key bounding surfaces which subdivide the strata into contemporaneously deposited sediment packages were identified from well log responses from a complete suite of logs which included Gamma Ray, Resistivity and Porosity logs. It was observed that shales in the TSTs were of higher acoustic properties compared to sales in the HSTs

Evaluating the Effects of Real Estate Development in Owerri, Imo State, Nigeria: Emphasizing Changes in Land Use/Land Cover (LULC)

Analysis of the impacts of real estate development on biodiversity within the confines of Imo State, Nigeria, was the main objective of this study. The investigation included a look at how land use and land cover (LULC) changed between 2017 and 2022. The study made use of Sentinel-2 image with a spatial resolution of 10 m. The research team used supervised classification algorithms to classify the imagery, which were then compared to find changes in land use and land cover (LULC). The following categories apply to the land use and land cover (LULC) of the study area: In 2017, trees accounted for 58.84 % of the total land surface and covered the most land, covering an area of 315.05 km2. The amount of developed land, or 30.23 % of the total land area, was assessed to be 161.84 km2. Approximately 61.91 % of the entire land surface in 2018, or 331.47 km2, was covered by arboreal vegetation, which dominated the landscape. Comparatively, urbanised regions made up 177.41 km2, or 33.14 % of the total land area. With trees making up 59.434 % or 318.22 km2 of the total land area in 2019, trees were found to be the most prevalent kind of land cover. Concurrently, built-up areas accounted for 34.30 % of the land, or 183.66 km2. The LULC map for 2020 showed a comparable pattern, with trees covering 58.46 % (equivalent to 313.02 km2) of the total land area and built-up areas covering 34.71 % (equivalent to 185.88 km2). According to the research, the impact of real estate development on the environment is primarily negative, resulting in habitat depletion, ecosystem fragmentation, and the introduction of pollutants. The researchers advised using sustainable development practises to mitigate the aforementioned negative effects

Solid Mineral Potential and Geothermal Energy Reserve of Northern Basement Complex, Nigeria

The spectral analysis of aeromagnetic data over the study area (Northern basement complex, Nigeria) revealed the existence of two major source depths. The second segment of the spectrum layer shows shallow sources ranging in depth from 0.135 km to 0.201 km, with an average depth of 0.140 km. The depth to the basement or deeper source ranges between 1.655 km and 2.021 km, with an average depth of 1.882 km. The Precambrian basement is reflected in the deeper sources, beginning with the first segment of the power spectrum. Structural and topographic relief of the basement surface, lateral differences in basement susceptibilities, and intra-basement features such as faults and fractures contribute to the variation in basement composition. The mean thickness of sediments in the study area is represented by the D2 values obtained from the spectral plots. The depths revealed by this study appears to be reasonable and consistent with previous researchers’ findings. Tectonically active regions have a major impact on heat flows. The average heat flow in thermally normal continental regions is reported to be above 60 mW/m2. Values between 80 and 100 mW/m2 indicate a good geothermal source; values in excess of about 80–100 mW/m2 indicate anomalous geothermal conditions