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Amplitude variation with offset (AVO) analysis was carried out on Konga oil field, an onshore oil field in the Niger Delta, Southeastern Nigeria. The study consisted of forward modeling from rock parameters measured from well logs and AVO analysis of events on pre-stack time migrated 3D seismic gathers. Forward modeling predicted specific AVO behav...
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... In recent days, AVO analysis has been widely applied in hydrocarbon exploration studies [18][19][20][21][22] . In the Niger Delta, AVO techniques have been used to determine anomalous and gas zones in wells [23][24][25][26][27] , detect hydrocarbon reservoirs on pre-stack time seismic data [28] , and identify hydrocarbon-charged reservoirs using Rock physics modeling and Lamda-Mu-Rho (LMR) seismic inversion [28] . The focus of the research has primarily been on conducting AVO Inversion analysis on pre-stack seismic data and well information to reveal geologic structures and impedance contrasts within lithology. ...
Amplitude Variation with Offset (AVO) inversion analysis was performed on pre-stack seismic data and well information gathered from the shallow offshore area of the Niger Delta. This analysis aimed to improve reservoir visualization and employed the Hampson Russell Geoview, AVO, and STRATA software tools. The seismic data were provided in Seg-Y format, covering an in-line range from 4503 to 5569, an x line range from 1434 to 2026, and an angle of incidence range of 0 to 45°. The study centered on the Taje well_026. Within the subsurface, the authors identified five distinct reservoirs, labeled A to E, located at various depths ranging from 3057.50 to 3115.00 m, 3115.00 to 3157.50 m, 3157.50 to 3190.00 m, 3190.00 to 3200.00 m, and 3200.00 to 3239.00 m, respectively. These reservoirs exhibited different fluid compositions. Reservoir A, primarily composed of sandstone, contained brine, whereas Reservoirs B and D, dominated by shale, contained gas. On the other hand, Reservoirs C and E, both comprised of sandstone, held oil. Reservoir C is distinguished by its clean sandstone unit. The inversion results revealed that both Reservoirs C and E consisted of low impedance sand layers surrounded by higher impedance shale layers. The gas migrated from the reservoir and was trapped within the shale units due to deformation of the lithological units, likely induced by stress accumulation. This migration process was facilitated by the shale’s inability to undergo smearing, possibly as a result of faulting mechanisms.
... Pre-stack inversion delivers a breakthrough in the analysis and interpretation of seismic amplitude data by extracting both compression and shear wave properties information (Goodway et al. 1997;Ohaegbuchu and Igboekwe 2016). Various studies prove that given the same geologically accurate information, pre-stack simultaneous inversion constraints with rock physics analysis provide more value to the interpreter to estimate reservoir petrophysical properties and lithofacies (Durrani et al. 2020b;Yi et al. 2018;Jiang and Spikes 2016;Shuangquan et al. 2009;Ma 2002). ...
The paper demonstrates a successful application of Bayesian classification method to accurately predict petrophysical properties and lithofacies classification in the deep unconventional (tight gas) hydrocarbon resource potential of early Cretaceous in the Lower Indus Basin of Pakistan. To explore the true potential for exploration and development phases, we quantitatively characterized the tight gas reservoir based on an integrated methodology using the Bayesian approach constraint with rock physics analysis which utilized deterministic petrophysical results from a well information to extract the desired lithofacies at seismic scale. The employed methodology relied on stepwise sequential integration of all available data through petrophysical, rock physics analysis and seismic inversion technique. Simultaneous inversion approach is used to invert elastic properties for reservoir interpretation. Seismic-based petrophysical properties are predicted using regression analysis by establishing a functional relationship between well logs for Sembar formation. The rock physics template (acoustic impedance versus Vs/Vs ratio) model helped to differentiate lithological units of sand and shale in the well. Three lithofacies (HC sands, shale and shalier sand) are properly classified in rock physics template, and their probabilities are accurately defined using Bayes’ theorem. Finally, estimated lithofacies and hydrocarbon probability map from the Bayesian approach are meticulously validated from well data. The quantitative seismic reservoir characterization study provided important support for the unconventional prospect evaluation and hydrocarbon reserve estimations necessary to delineate unexplored parts which could prove helpful in effectively planning for the horizontal well placement and optimal reservoir development.
... Accurate analysis is required to establish the AVO classes of reservoirs present in the study field, making it possible to concentrate on some particular scenarios and execute case specific analysis. Although several types of AVO reservoirs might possibly co-exist at the same time in the same field, evaluating all will make the assessment of the AVO response more timeconsuming (Paul and Marianne 2006;Uko and Emudianughe 2014;Avseth et al. 2016;Ogararue and Anine 2016;Ohaegbuchu and Igboekwe 2016;Toshe, 2017). Robinson et al. (2005) in their work discovered that fluid substitution is a necessary aspect of interpreting seismic-amplitude and AVO anomalies. ...
The application of quantitative interpretation techniques for hydrocarbon prospect evaluation from seismic has become so vital. The effective employment of these techniques are dependent on several factors; the quality of the seismic and well data, sparseness of data, the physics of rock, lithological and structural complexity of the field.
This study adopts Reflection pattern, Amplitude Versus Offset (AVO), Biot-Gassman Fluid Substitution and Cross-plot models to understand the physics of the reservoir rocks in the field by examining the sensitivity of the basic rock properties; P-wave velocity, S-wave velocity and Density, to variation in lithology and fluid types in the pore spaces of reservoirs. This is to ascertain the applicability of quantitative seismic interpretation techniques to explore for hydrocarbon prospect in the studied field. The results of Reflection pattern and AVO models revealed that the depth of interest is dominated by Class IV AVO sands with a high negative zero offset reflectivity that reduces with offset. The AVO Intercept versus Gradient plot indicated that both brine and hydrocarbon bearing sands can be discriminated on seismic. Fluid Substitution Modelling results revealed that the rock properties will favorably respond to variation in oil saturation but as little as 5% gas presence will result in huge change in the rock properties, which will remain constant upon further increments of gas saturation, thereby making it difficult to differentiate between economical and sub economical saturations of gas on seismic data. Rock Physics cross plot models revealed separate cluster points typical of shale presence, brine sands and hydrocarbon bearing sands. Thus the response of the rock properties to the modelling processes adopted favors the application of quantitative interpretation techniques to evaluate hydrocarbon in the field.
KeyWords: AVO Modelling, Fluid substitution, Quantitative interpretation, Rock Physics, Cross-plot Analysis.
... This basin is very intricate and has a high economic value since it contains a prosperous petroleum system. The filling of sediments has a depth between 9 and 12 km (Ohaegbuchu and Igboekwe 2016). It is consisting of several different geological formations indicating how this basin might have developed, as well as the area's regional and largescale tectonics (Aigbedion et al. 2007;Inyang et al. 2018). ...
Formation factor evaluation was carried out across eight reservoirs in five wells in a field in the Niger Delta region of Nigeria. Formation factor is very vital in assessment of petrophysical parameters, it is important to use in-situ wireline logs to evaluate the petrophysical constants. The identified reservoir in the study area show good accumulation of hydrocarbon having net pay zone between 5.00 and 83.50 ft and density porosity between 0.17 and 0.35. In-situ log was used to evaluate porosity exponent with the help of Pickett's plot, the porosity exponent values are between 1.65 and 2.52 in the study area. These values of porosity exponent are seen to be influence by the values of volume of shale, this indicates that the porosity exponent is affected by grain size, pore space and pore geometry. A modified formation factor equation from the Archie's equation using porosity exponent from Pickett's plot was used for formation evaluation of each of the wells. Most of the wells showed an over estimation (between 2.06 and 2.52) of porosity exponent expect for well BS02 (1.65) that indicates under estimation of the porosity exponent. The over estimation of formation factor as compared to Archie's equation is between 8.89% (BS01 R1) and 48.88% (BN01 R2), while for under estimation of formation factor as observed in well BS02 is between 39.02 (BS02 R1) and 41.29% (BS02 R2). The study has been able to propose an average formation factor equation as F = 1 2.17 for the delineated reservoir in the study area.
... In this research, by linking elastic rock properties to sedimentological parameters through the integration of rock physics and sequence stratigraphy, we can better understand the stiffness of the rock and predict with a higher degree of certainty what fluid lies within, since Rock Physics models do not only tell us about fluid sensitivities obtained from AVO analysis but also informs us of how seismic properties change as a function of geological texture (Amir et al., 2020. Amaechi et al., 2019Ajanaku and Akintorinwa 2019;Ohaegbuchu and Igboekwe, 2016). ...
The estimation of seismic reservoir properties faces difficulties such as delineating deceptive fluid contacts, over-estimating reserves and drilling dry holes. To overcome these problems, sequence stratigraphic and rock physics analyses that were constrained by the compaction trend were implemented in the Sigma Field. Sigma field is located in the Coastal Swamp depobelt, in the distal part. Seismic data from the field and a total of three wells, Sigma-30, 26, and 25 were provided for the analyses. Biostratigraphic data was also given for the field and dated to shale marker Cassidulina-7. The stratigraphic sequence analysis of the wells in field indicate they were deposited in a regressive, transgressive and aggradational episodes, whereas the entire depositional history shows predominantly regressive episode. Three field reservoirs, SM-14 (LST), SM-04 (TST), and SM-02 (HST) were delineated. Depositional analysis of the landscape from sequence stratigraphic analyses of the field indicates that Sigma field reservoirs were deposited in a front region of the delta. Trend to compaction has been generated for the well. Sigma wells showed a normal trend toward compaction. Rock physics analysis of the reservoirs of interest reveals that LST and TST reservoirs have been difficult to distinguish against HST reservoirs which are easy to classify regardless of their depth for fluids in a Vp/Vs crossplot versus acoustic impedance. In the analysis, a pattern is suggested for TST reservoirs. Therefore, this research work shows that fluid discrimination becomes simpler as we go basin ward as the sorting of the reservoir rocks increases with finer grain sand sizes.
... Many researchers reported on the modelling of the rock mass materials and its behaviour using neural networks (Ghabousi et al. 1991;Haykin 1998;Singh et al. 2001Singh et al. ,2003Bhatnagar and Khandelwal 2012;Salimia et al. 2015;Momeni et al. 2015;Tripathy et al. 2015;Madhubabu et al. 2016;Abdi et al. 2018;Rastegarnia et al. 2018). They also developed correlations between geological strength index, brittleness index and temperature using various statistical approaches (Fajana 2020;Fatoba et al. 2020;Ohaegbuchu and Igboekwe 2016;Ardestani et al. 2019;Jamshidi 2018;Aka et al. 2018;Akbarpour and Abdideh 2020;Dolui et al. 2016;Ghosh et al. 2016;Salehin et al. 2020;Dehkordi et al. 2019;Kumar et al. 2018aKumar et al. , b, 2019aKumar et al. , b, c, d, 2020a. Rumelhart et al. (1986) proved that the ANN model was perfect for classifying complex information for the demands of a new situation (Simpson 1990). ...
Determining properties of rocks in rock mechanics/engineering applications such as underground tunnelling, slope stability, foundations, dam design and rock blasting is often difficult due to the requirements of high quality of core rock samples and accurate test apparatus. Prediction of the geomechanical properties of rock material has been an area of interest for rock mechanics for several years now. Nowadays, soft computing methods are used as a powerful tool to estimate the rock properties, cost and duration of the project. This has led to a lack of necessity to develop a model to predict rock properties in the field of rock mechanics. ANN (artificial neural network) models were developed to predict geomechanical properties of the sedimentary rock types using dominant frequencies of an acoustic signal during rock drilling operations. A set of experimental drilling operations test conditions around 875 were used as input parameters including drill bit spindle speeds (rpm), drill bit penetration rates (mm/min), drill bit diameters (mm) and dominant frequencies of the acoustic signal (Hz). The response (output) was uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), density (\(\rho \)) and abrasivity (%). The developed models were checked using various performance indices. The results from the analysis show that the suggested ANN model approach is efficient, fits the data and accurately reflects the experimental results. The ANN models predicted physico-mechanical rock properties through the dominant frequency of acoustic signals during rock drilling operations.
... The technique has been adopted successfully for shear wave estimation and field evaluation across the word (Assefa et al., 2003;Paul and Marianne, 2006;Veeken, 2007;Chopra and Castagna, 2014;Toshev, 2017), but just few have been recorded of the Niger Delta Basin (Ekwe et al., 2012;Uko and Emudianughe, 2014;Ohaegbuchu and Igboekwe, 2016;Adeoti, et al., 2018 ;Ogbamikhumi et al., 2018 ). ...
This study applied Gassman’s fluid substitution theory to model the sensitivity of several saturations of gas and oil in the C4 reservoir using well log data, to determine the applicability of rock physics analysis to evaluate the reservoir in the undrilled area of the field. Results of fluid substitution modeling for gas revealed that P-wave velocity decreased significantly at 5% gas saturation. Upon further increments in the gas saturation, the P-wave velocity remained relatively constant, revealing the difficulty in differentiating between economical and sub- economical gas saturation on seismic. Oil substitution showed that as the saturation of oil introduced into the reservoir increases, the P-wave velocity and density decreases accordingly. The 80% oil saturation model appeared to be greater than the insitu P-wave and density. Hence it can be concluded that the insitu oil saturation may be greater than 80% oil. In both fluid substitution scenarios, the S-wave models remained constant irrespective of the fluid saturation. Cross plot of Vp/Vs ratio against P-impedance and Mu-rho against Lambda-rho technique were done to validate the modeling results. The techniques discriminated both the lithology types and fluid contrast in the reservoir. Hence, it can be applied in the evaluation and characterization of the C4 reservoir in the undrilled area of the field from inversion results.
KeyWords: Forward-Modelling, Fluid-Substitution, P-wave, S-wave, Fluid-Saturation, Rock Properties, Petrophysical Evaluation.
... The log data were subjected to some quality checks after loading to improve upon their quality to obtain desirable results. Some of the quality Castagna's empirical relationship [9], that relate P-wave and S-wave velocity for unconsolidated and moderately consolidated sand was applied first to estimate the correct shearwave velocity for the layers across the well at 100% Sw; i.e. for shales and brine filled sands [19], of the entire depth of the well. The equation is given in equation (1) (2) and ( 3)} required to estimate P-wave for the brine filled scenario [21]. ...
Shear wave logs are one of the key data required for most Amplitude Versus Offset (AVO) studies and quantitative seismic interpretation. Despite its overwhelming importance, it is hardly available in most wells due to its very high cost of acquisition. In the absence of shear wave logs in the field, the Castagna’s empirical relationship that relates P-wave and S-wave velocities and Biot-Gassman’s Fluid substitution equations were adopted for its estimation. The results obtained revealed that Castagna’s empirical relationship underestimated the Shear wave log in the field, even after Biot-Gassman’s Fluid substitution model was applied to define P-wave of the brine filled scenario. A cross plot of Mu-Rho versus Lambda-Rho presented low values for Mu-Rho due to the underestimated shear wave used. To correct for the underestimation, a modified form of the Castagna’s Equation constants were establish. This was achieved by generating several linear regression equations that defines the relationship between P and S-wave for brine sands in the field. The cross plot of Mu-Rho versus Lambda-Rho done with the modified shear wave gave very good results as expected for both fluid and lithology discrimination.
KeyWords; Shear Wave, Castagna’s Equation, Biot-Gassman’s Equation, Rock Physics, Cross plots
