Petroleum Engineering – Exploration
Mehrdad Safarpour; Mohammad Ali Riahi; Mehran Rahimi
Abstract
The main purpose of this paper is to estimate and evaluate the petrophysical properties of the Ghar Formation in the Hindijan and Bahregansar oilfields using a combination of seismic and well logs data. In this study, following a step-by-step regression approach: first; sonic, density, and, porosity ...
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The main purpose of this paper is to estimate and evaluate the petrophysical properties of the Ghar Formation in the Hindijan and Bahregansar oilfields using a combination of seismic and well logs data. In this study, following a step-by-step regression approach: first; sonic, density, and, porosity well-log data are collected. Second; seismic attributes, including amplitude, phase, frequency, and acoustic impedance are extracted from the seismic lines intersecting the wellbore locations. Then, using the MFLN and PNN intelligent systems, a relationship between porosity, shale volume, saturation, and seismic attributes is established. Using this relationship, the physical and petrophysical properties of the reservoir in the Ghar Formation are estimated and evaluated. We estimated the reservoir porosity between 15% and 20%, which is higher in the Hendijan oilfield as compared to the Bahregansar oilfield. The amount of water saturation in the Ghar formation varies between 25 and 30 percent. On the other hand, the amount of clay content and shale volume of the Ghar Formation in the Hendijan field is higher than that of the Bahregansar oil field.
Petroleum Engineering – Exploration
Ali Jelvegarfilband; Mohammad Ali Riahi; Majid Bagheri
Abstract
The petrophysical parameters of the Ghar Formation are characterized in this study. A combination of pre-stack seismic data gathers and well-log data is used to estimate water saturation and shale volume in the Ghar reservoir. For such a purpose, first, the highest possible correlation between the well ...
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The petrophysical parameters of the Ghar Formation are characterized in this study. A combination of pre-stack seismic data gathers and well-log data is used to estimate water saturation and shale volume in the Ghar reservoir. For such a purpose, first, the highest possible correlation between the well logs and the seismic inverse data was established. After extracting the best wavelet, an accurate relationship between the estimated and the values from core data was obtained. Secondly, using the data of another well, the validity of the constructed model was examined. The results showed that the combination of three attributes of instantaneous cosine of phase, √(Z_P ), and √(V_P ) is suitable to estimate the shale volume of the reservoir with considerable accuracy with a correlation coefficient of about 70%. Although the two layers in the Ghar section have a shale volume of about 10%, in general, the shale volume in the reservoir area is negligible. The logarithm of the ratio of compressional wave velocity to shear wave velocity attribute shows the highest correlation, about 62%. Finally, validation of the results of the mentioned properties with unintroduced well-log data showed an accuracy of about 90% in prediction.
Petroleum Engineering
James Sunday Abe; Kenneth Okosun
Abstract
Modelling involves the use of statistical techniques or analogy data to infill the inter-well volume producing images of the subsurface. Integration of available data sets from “KO” field were used to identify hydrocarbon prospects and by means of interpolation, populate the facies and petrophysical ...
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Modelling involves the use of statistical techniques or analogy data to infill the inter-well volume producing images of the subsurface. Integration of available data sets from “KO” field were used to identify hydrocarbon prospects and by means of interpolation, populate the facies and petrophysical distribution across the field to define the reservoir properties for regions with missing logging data[KO1] . 3D seismic data, check-shot data, and a series of well logs of four wells were analyzed, and the analysis of the well logs was performed using the well data. The synthetic seismogram produced from the well ties [M.N.2] [KO3] was used to map horizon slices across the reservoir regions. Four horizons and fifteen faults, including one growth fault, four major faults, and other minor faults, all in the time domain were mapped. Attribute analyses were carried out, and a 3D static model comprised of the data from the isochore maps, faults, horizons, seismic attributes, and the various logs generated was built. A stochastic method was also employed in populating the facies and petrophysical models. Two hydrocarbon-bearing sands (reservoirs S1 and S2) with depth values ranging from –1729 to 1929 m were mapped. The petrophysical analysis gave porosity values ranging from 0.18 to 0.24 across the reservoirs, and the permeability values ranged from 2790 to 5651 mD. The water saturation (Sw) of the reservoirs had an average value of 50% in reservoir S1 and 47% in reservoir S2. The depth structure maps generated showed an anticlinal structure in the center of the surfaces, and the mapped faults with the four wells were located in the anticlinal structure. The reserve estimate for the stock tank oil initially in place (STOIIP) of the reservoirs was about 70 mmbbl, and the gas initially in place (GIIP) of the reservoirs ranged from 26714 to 63294 mmcf. The result of the petrophysical analysis revealed the presence of hydrocarbon at favorable quantities in the wells, while the model showed the distribution of these petrophysical parameters across the reservoirs. Modelling involves the use of statistical techniques or analogy data to infill the inter-well volume producing images of the subsurface. Integration of available data sets from “KO” field were used to identify hydrocarbon prospects and by means of interpolation, populate the facies and petrophysical distribution across the field to define the reservoir properties for regions with missing logging data[KO1] . 3D seismic data, check-shot data, and a series of well logs of four wells were analyzed, and the analysis of the well logs was performed using the well data. The synthetic seismogram produced from the well ties [M.N.2] [KO3] was used to map horizon slices across the reservoir regions. Four horizons and fifteen faults, including one growth fault, four major faults, and other minor faults, all in the time domain were mapped. Attribute analyses were carried out, and a 3D static model comprised of the data from the isochore maps, faults, horizons, seismic attributes, and the various logs generated was built. A stochastic method was also employed in populating the facies and petrophysical models. Two hydrocarbon-bearing sands (reservoirs S1 and S2) with depth values ranging from –1729 to 1929 m were mapped. The petrophysical analysis gave porosity values ranging from 0.18 to 0.24 across the reservoirs, and the permeability values ranged from 2790 to 5651 mD. The water saturation (Sw) of the reservoirs had an average value of 50% in reservoir S1 and 47% in reservoir S2. The depth structure maps generated showed an anticlinal structure in the center of the surfaces, and the mapped faults with the four wells were located in the anticlinal structure. The reserve estimate for the stock tank oil initially in place (STOIIP) of the reservoirs was about 70 mmbbl, and the gas initially in place (GIIP) of the reservoirs ranged from 26714 to 63294 mmcf. The result of the petrophysical analysis revealed the presence of hydrocarbon at favorable quantities in the wells, while the model showed the distribution of these petrophysical parameters across the reservoirs. [KO1]Sentence has been rephrased. [M.N.2]This verb does not make sense in this context and has made the sentence unclear. [KO3]Sentence has been rephrased
Majid Bagheri; Mohammad Ali Riahi
Abstract
Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has ...
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Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases associated with supervised classification methods. In this study, we follow supervised classification scheme under classifiers, the support vector classifier (SVC), and multilayer perceptrons (MLP) to provide an opportunity for directly assessing the feasibility of different classifiers. Before choosing classifier, we evaluate extracted seismic attributes using forward feature selection (FFS) and backward feature selection (BFS) methods for logical SFA. The analyses are examined with data from an oil field in Iran, and the results are discussed in detail. The numerical relative errors associated with these two classifiers as a proxy for the robustness of SFA confirm reliable interpretations. The higher performance of SVC comparing to MLP classifier for SFA is proved in two validation steps. The results also demonstrate the power and flexibility of SVC compared with MLP for SFA.
