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
Petroleum Engineering – Exploration
Benyamin Khadem; Abdolrahim Javaherian
Abstract
Reservoir characterization has a leading role in the reservoir geophysics and reservoir management. Since the interests of the reservoir geophysics and reservoir managements are the elastic properties and reservoir properties of the subsurface rock for their purposes, a robust method is required for ...
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Reservoir characterization has a leading role in the reservoir geophysics and reservoir management. Since the interests of the reservoir geophysics and reservoir managements are the elastic properties and reservoir properties of the subsurface rock for their purposes, a robust method is required for converting seismic data into elastic properties. Accordingly, by employing a rock physics model and using the inverted seismic data, one can describe the reservoir for purposes such as improvement in the production of the reservoir. In the present study, we employ one of the methods for converting the seismic data into the elastic properties. This method of inversion is known as simultaneous inversion, which is grouped in amplitude-variation-with-offset (AVO) inversion category. In this method, unlike the other methods of AVO inversion, the pre-stack seismic data are directly inverted into the elastic properties of the rock and an excellent lithology and fluid indicator (VP/VS) are provided. Then, this indicator is tested on one of the oilfields of the Persian Gulf. Moreover, by means of this method, one can locate the fluids contact and the lithological interlayers; also, by the inversion results, which are the cubes of the seismic properties of the rock, one can generate sections of the elastic properties of the rock such as Poisson’s ratio and Young modulus which are useful for geomechanical analysis. Therefore, this kind of method is a quick way for the prior analysis of the studied area.
Petroleum Engineering – Exploration
Syed Waqas Haider; Mustafa Yar; Raja Ahtisham Ghafoor; Tallat Majeed Khan
Abstract
The well Sarai Sidhu-01 is located on Punjab Platform, Central Indus Basin, Pakistan. Punjab Platform is the eastern part of Central Indus Basin, and tectonically it is the stable portion of Indus Basin, which was least affected during Tertiary Himalayan orogeny. This study attempts to decipher reservoir ...
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The well Sarai Sidhu-01 is located on Punjab Platform, Central Indus Basin, Pakistan. Punjab Platform is the eastern part of Central Indus Basin, and tectonically it is the stable portion of Indus Basin, which was least affected during Tertiary Himalayan orogeny. This study attempts to decipher reservoir potential for hydrocarbon exploration. It aims to delineate a subsurface hydrocarbon bearing zone and to estimate the reservoir properties. A complete suite of wireline logs containing Caliper log (CALI), gamma ray log (GR), spontaneous potential log (SP), neutron log (ØN), density log (ØD), and resistivity logs (MSFL, LLS, and LLD) with all drilling parameters and well tops were utilized. The methodology adopted to accomplish this task includes the calculation of volume of shale (Vsh) by using gamma ray log and effective porosity (ØE) by using density and neutron logs. Resistivity of water (Rw) was calculated by SPmethod, and the saturation of water (Sw) and the saturation of hydrocarbons (Sh) is calculated with the help of Archie’s equation. According to log signatures, Lumshiwal formation of early Cretaceous age encountered in well in the depth range of 5433 ft. to 5797 ft. was marked as a possible reservoir, and this zone was evaluated for its reservoir potential in detail using a set of equations. The average values calculated for different parameters are as follows: Vsh= 30%, ØE= 17%, Sw= 46%, and Sh= 54%. The analysis shows that Sh is low, so it is inferred that Lumshiwal formation has a low potential and is economically not feasible for hydrocarbons production.
