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Wireline log data for Well-06 showing suite of logs Geophysical Tool/ Technology (Hampson Russell Software): Hampson Russell Software was used for the analysis of this work. It consists of quite a number of modules, some of which include the Geoview which serves as a starting point of any Hampson Russell program. Well log data are loaded into Geoview well database through the Well Explorer.
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The cross plotting of rock properties for fluid and lithology discrimination was carried out in a Niger Delta oil field using well data from a given oil field. The data used for the analysis consist of suites of six wells. The reservoir was evaluated using gamma ray logs, volume of shale, resistivity and neutron porosity for well-02 and well-06. Sa...
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... These include acoustic impedance (AI), which is the product of density and compressional wave velocity, the shear wave and compressional wave velocities (vp and vs, respectively), Shear impedance (SI) which is the product of shear wave velocity and density.All of the equations used for these computations are listed below [1], [7], [8], [16]. [17], [18], [19], [20], [21], [22]. [23] ii. ...
The main components of reservoir rocks are hydrocarbons and immiscible water in varying ratios. It is essential to precisely identify, characterize, and divide the fluids in these reservoirs into distinct groups according to the characteristics of their rock properties to conduct a successful hydrocarbon exploration. For this reason, petrophysics and rock physics analysis were combined on the "NICK" field in the onshore Niger Delta. Through precise litho-fluid discrimination in the field, this study seeks to improve field hydrocarbon production, lower uncertainty, and mitigate risks related to hydrocarbon exploration. The suites of well logs (Gamma-ray neutron, bulk density, sonic, and resistivity logs) from three wells—NICK-1, NICK-3, and NICK-6—make up the data used. Gamma-ray log signatures were used to identify and correlate lithologies throughout the field. Potential reservoirs and fluid content were identified and delineated by high resistivity and adequate neutron-porosity log signatures. Hydrocarbon-bearing sands were recorded at low values of elastic attributes (acoustic impedance, rigidity, incompressibility, and others), which were computed to aid in the characterizations. Two potentialreservoirs’ Sands A and B delineated, constituted the correlated pay zones observed in three wells across the field at depths ranging from 1986.24 to 2599.82m. Petrophysics results generally revealed fair to good porosities of (11-25%) for easy accumulation of hydrocarbon. Permeability ranged from 210- 809mD for Sand A and 27 – 887mD for Sand B, showing that there are suitable permeabilitiesfor fluid movement/migration within the reservoirs. Cross plot of Lambdarho versus Murho, Lambdarho versus Velocity Ratio, and Velocity Ratio versus Acoustic Impedance gave four distinct clusters for litho-fluid zones identification given as gas-sand, oil-sand, brine-sand, and shale. This study has assisted in better characterization and distinguishing of the litho-fluid details for enhancement of hydrocarbon production in the field.
... Furthermore, [16] conducted a study on cross plotting rock properties for fluid and lithology discrimination in a Niger Delta oil field. Their research emphasized the importance of characterizing hydrocarbon reservoirs accurately in terms of fluid properties and lithology. ...
... The scope of work undertaken in this research encompasses a thorough literature survey of previous studies and research in the field of seismic reservoir characterization. We have analyzed the contributions of [8] in establishing seismic reservoir characterization using Lamé parameters, [12] in applying statistical rock physics and seismic inversions, [14] in highlighting the role of rock physics integration, [14] in examining the impact of quartz cement and fluid changes, [15] in crossplot analysis of Vp:Vs ratios, [16] in crossplot analysis for fluid and lithology discrimination, [9] in Reservoir characterization using Amplitude Versus Offset (AVO) analysis and [7] Reservoir characterization and analysis of fluid behavior. ...
... This crossplot aids in understanding the fluid content and distribution in the reservoir and assists in the identification of gas-sand and wet-sand intervals. These figures demonstrate the utility of crossplots in distinguishing different fluid types and identifying hydrocarbon-bearing intervals within the field [16]. ...
... With the in-depth study and application of statistical analysis and complex network theory, crossplot [28][29][30] and ordinal pattern transition networks (OPTN) [31][32][33][34] are emerging as major tools for nonlinear time series interrelationship analysis and are finding meaningful applications in the analysis of experimental data in a variety of research fields. Crossplot is a graphical representation of time series in the Cartesian plane. ...
To evaluate the synchronization of bivariate time series has been a hot topic, and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network into the crossplot, a new method for measuring the synchronization of bivariate time series is proposed. After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model and compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitability for short time series. Finally, electroencephalogram (EEG) data from auditory-evoked potential EEG-biometric dataset are investigated, and some useful and interesting results are obtained.
... "The economic viability of a hydrocarbon field is reliant on the quality, quantity, and accuracy of lithology and pore fluid" [1]. "Accurate description, evaluation, and prediction of a reservoir in terms of lithology and fluid content is an important factor in reducing the risk involved in hydrocarbon exploitation and exploration" [2,3]. "There has been a growing interest in determining lithology and pore fluid using well log data, which is cheaper, more reliable, and economical. ...
... The aim of the study is to carry out fluid and lithology discrimination of HAX field, offshore, Niger Delta by (1) delineating reservoir sand bodies using well logs in the study area, (2) computing the elastic rock attributes such as Lame's parameters terms (λρ and μρ), Vp/Vs ratio, P-Impedance, and S-Impedance from available petrophysical data obtained from the well logs, (3) cross-plotting petrophysical and elastic rock attribute to delineate fluid and lithology in the reservoirs from well log, and (4) determine which attributes best discriminate fluid and lithology. ...
... In theory, sand will have a high value of Mu-Rho and a low value of shale [38] but in this field, the results of the cross-plots show that the Mu-Rho values are high for shale and low for sand while the density of shale is higher than that of sand [2,33]. Furthermore, hydrocarbon bearing sand is less dense than brine (even as hydrocarbon gas is less dense than oil) and brine is less dense than shale [2,33]. ...
Fluid and lithology discrimination for reservoir characterization of HAX field, offshore Niger Delta was carried out in this study. Three reservoir intervals, R_4500, R_5500, and R_6500 were picked, identified, and correlated across the four wells; but only the R_5500 reservoir was analyzed. The cross-plot analysis of elastic rock properties with reservoir properties such as Vp/Vs ratio against Acoustic Impedance, Lambda-Rho against Vp/Vs, Mu-Rho against Density, and Lambda-Rho against Mu-Rho colour-coded by gamma ray, water saturation, and density respectively was carried out for fluid and lithology discrimination. The result of these elastic rock properties when colour-coded with gamma ray distinguished reservoir R_5500 into the sand zone and shale zone for the four wells, these results depict lithology discrimination as predominantly found in the Niger Delta basin. Consequently, when colour–coded by water saturation reservoir R_5500 was distinguished into three zones namely the hydrocarbon bearing zone, brine sand zone and shale zone indicative of both lithology and fluid discrimination. From these cross-plots, the clusters with the least water saturation correspond to highly charged hydrocarbon saturation sand while clusters with maximum water saturation correspond to non-hydrocarbon zone (brine sand and shale). Finally, when colour–coded by density reservoir R_5500 was distinguished into four zones namely gas sand zone and oil sand zone, brine sand zone, and shale zone indicating fluid types. The result shows relatively lower Acoustic Impedance, Vp/Vs ratio, lambda-rho, mu-rho, and density (as the colour-code) values indicating hydrocarbon bearing sand while the relatively higher Acoustic Impedance, Vp/Vs ratio, lambda-rho, mu-rho and density (as the colour-code) values are associated with non-hydrocarbon zone (shale and brine sand). This study has been able to discriminate hydrocarbon reservoirs using the cross-plots of elastic rock properties in the zone of interest and proven that the HAX field is viable in terms of hydrocarbon prospects and highly economical for production.
... However, the cross plot of the AI and Vp/Vs ratio can differentiate the fluid type and lithology more accurately than individual parameters. The cross plot of lambda-rho and Mu-rho can distinguish the fluid type and lithology [51]. ...
The information about the subsurface structure, type of fluids present in the reservoir, and physical properties of the rocks is essential for identifying potential leads. The integrated approach of petrophysical analysis, seismic data interpretation, seismic attributes analysis, lithology, mineralogy identification, and Gassmann fluid substitution were used for this purpose. The structural interpretation with the help of seismic data indicated the extensional regime with horst and graben structures in the study area. The two negative flower structures are cutting the entire Cretaceous deposits. The depth contour map also indicate favorable structures for hydrocarbon accumulation. The four possible reservoir zones in the Sawan-01 well and two zones in the Judge-01 well at B sand and C sand levels are identified based on well data interpretation. The main lithology of the Lower Goru Formation is sandstone with thin beds of shale. The clay types confirm the marine depositional environment for Lower Goru Formation. The water substitution in the reservoir at B sand and C sand levels indicated increased P-wave velocity and density. The water substitution affected the shear wave velocity varies slightly due to density changes. The cross plots of P-impedance versus Vp/Vs ratio differentiate the sandstone with low P-impedance and low Vp/Vs ratio from shaly sandstone with high values in the reservoir area. The P-impedance and S-impedance cross plot indicate increasing gas saturation with a decrease in impedance values. The low values of Lambda-Rho and Mu-Rho indicated the gas sandstone in the cross plot.
... Since oil and gas are the primary energy sources, many geoscientific studies have been carried out to explore and exploit available hydrocarbon reserves to fulfil the world's current energy demand (Ashraf et al., 2020;Reza et al., 2019;Rasaq et al., 2015). ...
... Net-To-Gross is a measure of the potential of the productive part of a reservoir. It is usually expressed either as a percentage or fraction of the producible (net) reservoir within the overall (gross) reservoir packages [26][27][28][29][30][31][32]. ...
... This is the space occupied by shale or the fraction of shale (clay) present in reservoir rocks, the volume of shale in a reservoir plays a key role in hydrocarbon production where the higher the reservoir shaliness, the poorer the reservoir productivity [27][28][29][30][31][32][33][34][35]. The magnitude of the gamma ray count in a formation of interest is related to the shale content of the formation and the relationship may be linear or non-linear. ...
An integrated approach of reservoir characterization of a field was performed using seismic attributes and petrophysical parameters for the evaluation of subsurface geological features and hydrocarbon potential of an onshore field in Niger Delta Basin. Four reservoir intervals were identified within the field wells based on their position within the stratigraphic column, and the reservoir correlation, which was aided using the principle of uniform horizontality, based on the simple rule that sediments are deposited horizontally and basic understanding of sequence stratigraphy. The study revealed that, the four reservoirs were predominantly sand units intercalated with shale within the reservoir units. The petrophysical evaluation revealed the Net to Gross (NTG) values ranges from 79% to 87% within the reservoir units, while the effective porosity ranges from 17% to 21%, the permeability ranges between 1307mD to 1678mD across the reservoir units, while the water saturation ranges from the lowest of 35% (Reservoir C) to 78% in reservoir D. A total of fifteen faults were interpreted using the seismic data, while the surface maps (Time and depth surface maps) revealed the identified closures which are anticlinal structures that are fault dependent. The characterization of the reservoir was further enhanced using the seismic attributes (structural and stratigraphic) extracted such as Reflection intensity, Sweetness, Variance, Envelope, Instantaneous frequency, Time gain, Trace AGC, Local structural dip, Gradient magnitude and RMS amplitude. The results shows moderate to high sweetness (sweet spots) within the zone of interest, while the Envelope attribute show acoustic impedance contrasts indicating discontinuities, lithology changes and possible present of hydrocarbon (Bright spots). The variances and gradient magnitude enhanced the signal to map out discontinuities caused by faults and fractures which are signature that enabled delineation of the zone. The integrated approach validates the lithology discrimination of the elastic properties from the well logs and its effectiveness in optimizing and proper understanding of the subsurface, thus identifying and unmasking hidden features within the reservoir (probable bypass) in the field. The study has revealed that the integration of seismic attributes with petrophysical parameters is a better characterization method for fluid and lithology discrimination of a field in any given reservoir study.
... Net-To-Gross is a measure of the potential of the productive part of a reservoir. It is usually expressed either as a percentage or fraction of the producible (net) reservoir within the overall (gross) reservoir packages [26][27][28][29][30][31][32]. ...
... This is the space occupied by shale or the fraction of shale (clay) present in reservoir rocks, the volume of shale in a reservoir plays a key role in hydrocarbon production where the higher the reservoir shaliness, the poorer the reservoir productivity [27][28][29][30][31][32][33][34][35]. The magnitude of the gamma ray count in a formation of interest is related to the shale content of the formation and the relationship may be linear or non-linear. ...
Reservoir characterization of a field was performed using petrophysical parameters for the evaluation of subsurface geological features and hydrocarbon potential of an onshore field in Niger Delta Basin. Four reservoir intervals were identified within the field wells based on their position within the stratigraphic column, and the reservoir correlation, which was aided using the principle of uniform horizontality, based on the simple rule that sediments are deposited horizontally and basic understanding of sequence stratigraphy. The study revealed that, the four reservoirs were predominantly sand units intercalated with shale within the reservoir units. The petrophysical evaluation revealed the Net to Gross (NTG) values ranges from 79% to 87% within the reservoir units, while the effective porosity ranges from 17% to 21%, the permeability ranges between 1307mD to 1678mD across the reservoir units, while the water saturation ranges from the lowest of 35% (reservoir C) to 78% in reservoir D. The approach validates the lithology discrimination of the elastic properties from the well logs and its effectiveness in optimizing and proper understanding of the subsurface, thus identifying and unmasking hidden features within the reservoir (probable bypass) in the field. The study has revealed that petrophysical parameters can be used quantitatively to characterize a field in terms of its lithology and fluid contents of the reservoir.
... The methodology employed include rock physics cross-plots analysis and attribute generation from seismic data using simultaneous inversion. The rock physics cross-plots are useful in determining the rock properties/attributes that better discriminate fluids in the reservoir [20,21]. To investigate the lateral variations of reservoir properties away from the well bore, integration of reservoir properties derived from log data with seismic attributes was done. ...
... Compressional wave velocity to shear wave velocity is recognised to be a good attribute in discriminating fluid type [23]. Lambda-Rho (λρ) and Mu-Rho (μρ) have been shown by various researchers in hydrocarbon reservoirs to be robust discriminators of lithology and pore fluid [20,24,25] Crossplots of Mu-rho (μρ) against Lambda-rho (λρ) colour coded with volume of shale (Vshale) as the third dimension ( Fig. 3 & 4), show data clusters separated into four distinct zones inferred to be probable gas zone (purple), oil zone (blue), and brine (black) and shale (red), validated by lowest Vshale values. Low values of Lambda-rho (λρ) and high values of Mu-rho (μρ) are associated with gas and oil saturations, while higher values of lambda-rho and low values of Mu-rho correspond to brine saturation and shale. ...
Prediction of elastic properties using crossplot analyses and seismic inversion quantitative interpretation in Zeta field was carried out to improve quantitative interpretation in the field. The dataset used comprises of well logs and full stack seismic data. Rock attribute properties modelled from well logs and analysed in the cross-plot space show strong sensitivity to reservoir lithology and fluids. The cross-plot of lambda-rho (λρ) versus mu-rho (μρ) distinguishes the reservoirs into shale, brine sands, oil sands and gas sands. The cross-plots show that hydrocarbon sands have low λρ and high μρ values. λρ is sensitive to fluid and μρ sensitive to rock matrix only. This makes μρ versus λρ a good fluid discriminator. This suggests that the inversion result could be useful to distinguish sands from shale and brine from oil. Simultaneous seismic inversion method was applied to transform seismic reflection data into quantifiable rock properties. Results reveal low lambda-rho values which are indicative of hydrocarbon saturated-sands. These values confirm the availability of hydrocarbons around Wells 3, 4 & 5 locations and to the Northwest of the horizon's slices. However, shale zones and brine are observed to the western part of the field and surrounding the hydrocarbon zones, indicating probable depleted zones and/or source rock. Moderately compacted clean hydrocarbon filled reservoir sands exhibits relatively high values of mu-rho due to their high opposition to shearing, while unconsolidated clean hydrocarbon saturated reservoir formations exhibit low mu-rho values. The inversion indicates that lambda-Rho attribute is more diagnostic of fluid characterization. This study can be applied in identification of new prospects as an effective economic and decision-making tool.
... Cross plot analysation is used to develop the relationship between impedance and porosity and helps to separate the reservoir part from the non-reservoir part. It is beneficial in distinguishing between lithology and fluid in reservoir (Rasaq et al. 2015). In matured reservoir zones sometimes requires unconventional investigation tools (Chopra et al. 2010). ...
The main purpose of this study is to estimate reservoir prediction. The inversion technique is used to calculate impedance from seismic data. Ambiguity is seen in transforming impedance principles into rock properties like porosity in seismic inversion. A good indication of the reservoir is when there is a high-pitched rise in porosity and low-slung impedance identified. Shale has low impedance like reservoir rock and excellent porosity, therefore, this phenomenon also represents the occurrence of shale. The study is focused on two different features i.e.to identify reservoir and non-reservoir rocks by cross plot analysis. The technique is used to resolve this ambiguity in which gamma-ray standards are surrounded during porosity prediction from the impedance. Our method prevents misleading the shale points. To achieve the target in the first attempt it requires high exploration costs. Optimization technology innovates higher-value solutions. Exploration risk is mitigated by reservoir prediction through the inversion technique. It involves the change of impedance into quantitative rock properties. The generated cross plot of the attributes are reasonably penetrating to lithology and fluid judgment in the reservoir is used for investigation. GR values with porosity identify the lithology contrast. GR scale for lithology identification of coarser, medium and fine exist in different ranges as given below, (1-35) API for clean sand (45-65), API for poor porosity, (35-45) API for average porosity, (65-75) API for silt, and (>75) API for shale. It noticeably expressions a great level parting of the lithologies as well as the reservoir fluid fillings.
