Petroleum Engineering – Exploration
Samuel Getnet Tsegaye
Abstract
ABSTRACT The lithofacies and environments of deposition interpretations of the Calub-Hilala field towards central trough of Ogaden Basin have been carried out. Geophysical well logs from three deep exploration wells; Calub-1, Bodle-1 and Hilala-2 were used. A methodology was piloted in establishing the ...
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ABSTRACT The lithofacies and environments of deposition interpretations of the Calub-Hilala field towards central trough of Ogaden Basin have been carried out. Geophysical well logs from three deep exploration wells; Calub-1, Bodle-1 and Hilala-2 were used. A methodology was piloted in establishing the sedimentary facies, their successions and environments of deposition. Gamma ray, neutron, sonic and resistivity logs were used for lithologic and depositional environment identification respectively. An attempt were also made to identify formation tops and well to well lithostratigraphic correlation basing gamma ray log trends and correlating with cored interval of the wells for Lithological comparisons. Lithofacies interpretation was carried out with Schlumberger’s Petrel 2009TMsoftware. Correlation techniques were conducted to delineate the subsurface trends of these facies with electrafacies to compare facies interpretation results that were implied using the wire line log signatures.Ten (10) formations; Calub, Bokh, Gumburo, Adigrat, Transition, Hamanlei (Lower, Middle and Upper), Urandab, Gebredare, Gorrahei, Mustahil and Five (5) log facies; a cylindrical-shaped log trends representing aeolian, braded fluvial; a funnel-shaped facies representing a crevasse splay; a carbonate shallowing upward sequence and shallow marine sheet sand; bell-shaped facies representing transgressive marine shelf; a symmetrical- shaped facies representing sandy offshore and an irregular shaped facies representing fluvial floodplain were recognized. The environments of deposition delineated for the study area are alluvial and transgressive – regressive marine.
Petroleum Engineering – Exploration
Ziba Hosseini; Sajjad Gharechelou; Asadollah Mahboubi; Reza Moussavi-Harami; Ali Kadkhodaie-Ilkhchi; Mohsen Zeinali
Abstract
The conjugation of two or more Artificial Intelligent (AI) models used to design a single model that has increased in popularity over the recent years for exploration of hydrocarbon reservoirs. In this research, we have successfully predicted shear wave velocity (Vs) with higher accuracy through the ...
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The conjugation of two or more Artificial Intelligent (AI) models used to design a single model that has increased in popularity over the recent years for exploration of hydrocarbon reservoirs. In this research, we have successfully predicted shear wave velocity (Vs) with higher accuracy through the integration of statistical and AI models using petrophysical data in a mixed carbonate-siliciclastic heterogeneous reservoir. In the designed code for multi-model, first Multivariate Linear Regression (MLR) is used to select the more relevant input variables from petrophysical data using weight coefficients of a suggested function. The most influential petrophysical data (Vp, NPHI, RHOB) are passed to Ant colony optimization (ACOR) for training and establishing initial connection weights and biases of back propagation (BP) algorithm. Afterward, BP training algorithm is applied for final weights and acceptable prediction of shear wave velocity. This novel methodology is illustrated by using a case study from the mixed carbonate-siliciclastic reservoir from one of the Iranian oilfields. Results show that the proposed integrated modeling can sufficiently improve the performance of Vs estimation, and is a method applicable to mixed heterogeneous intervals with complicated diagenetic overprints. Furthermore, predicted Vs from this model is well correlated with lithology, facies and diagenesis variations in the formation. Meanwhile, the developed AI multi-model can serve as an effective approach for estimation of rock elastic properties. More accurate prediction of rock elastic properties in several wells could reduce uncertainty of exploration and save plenty of time and cost for oil industries.
Petroleum Engineering
Hessam MansouriSiahgoli; Mohammad Ali Riahi; Bahare Heidari; Reza Mohebian
Abstract
It is difficult to identify the carbonate reservoirs by using conventional seismic reflection data, especially in cases where the reflection coefficient of the gas-bearing zone is close to that of the carbonate background. In such cases, the extended elastic impedance (EEI) as a seismic reconnaissance ...
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It is difficult to identify the carbonate reservoirs by using conventional seismic reflection data, especially in cases where the reflection coefficient of the gas-bearing zone is close to that of the carbonate background. In such cases, the extended elastic impedance (EEI) as a seismic reconnaissance attribute with the ability to predict fluids and lithology can be used. It allows for a better distinction between seismic anomaly caused by lithology and the one caused by the fluid content. The EEI attribute extends the available reflection angles and applies different weights to the intercept and gradient values so as to extract the petrophysical properties of the rock at a specific incident angle. Using the EEI attribute, we can estimate the elastic parameters such as shear impedance; the ratio of the compressional velocity to shear velocity; Poisson’s ratio; and bulk, Lame, and shear moduli, and petrophysical properties, including porosity, clay content, and water saturation. The known reservoirs in the study area are three oil-bearing formations namely, Surmeh (Arab), Gadvan (Buwaib), and Dariyan (Shuaiba), and three gas-bearing formations, including Kangan, Dalan, and Faraghan. The Dehram group is composed of Kangan (Triassic), Dalan, and Faraghan (Permian) formations. Permian carbonates of Kangan–Dalan and its equivalent Khuff have regionally been developed as a thick carbonate sequence in the southern Persian Gulf region. In this paper, parameters 𝜆𝑝 and 𝜇𝜌 extracted from the EEI method are used to characterize a carbonate reservoir. Our results show that the EEI can highlight the difference between the reservoir and non-reservoir formation to identify the gas-bearing areas.
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.
