Iranian Journal of Oil and Gas Science and Technology

Abstract The primary purpose of this paper is to estimate and evaluate the petrophysical properties of the Ghar formation in the Hendijan and Bahregansar oilfields using a combination of seismic and well-log data. This study follows 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 to be between 15% and 20%, higher in the Hendijan oilfield than in the Bahregansar oilfield. The water saturation in the Ghar formation varied from 25% to 30%. On the other hand, the clay content and shale volume of the Ghar formation in the Hendijan field were higher than those of the Bahregansar oil field.

Abstract This study uses the kernel principal component analysis (KPCA) feature extraction method for facies classification to extract new features from the measured well logs. After applying the principal component analysis (PCA) and KPCA feature extraction approaches, the classification was made using three robust classifiers: multilayer perceptron neural network (MLP), support vector machine (SVM), and random forest (RF). Finally, the predicted results for the test data that were not included in the training process were evaluated with the F1 score criterion. The PCA method did not significantly affect the classification performance due to the nonlinear structure of the facies. Our results show that the KPCA improves the performance of facies classification. Compared with the conventional approach based on well-log data, our new approach improves the classification accuracy for each classifier algorithm. In the RF results, the classification accuracy has increased by about 6%, while using the KPCA feature extraction approach raises classification accuracy from 52% to 58% compared to the original well-log data.

Abstract In this study, the hydrocarbon potential and depositional environments of the Coniacian Laffan formation were investigated in the Binak oilfield, SW Iran. With an average thickness of 80 m, the Laffan formation consists mainly of gray shales and thin argillaceous limestones in the study area. In order to investigate the hydrocarbon potential, 22 cutting samples from 5 wells of the Binak oilfield were analyzed by Rock-Eval 6 pyrolysis and organic petrographic techniques. The hydrogen index (HI) versus T_max diagrams indicated mixed-type II/III kerogen with a maturity corresponding to the early stages of the oil window (T_max ≈ 435 °C). In addition, plots of S1+ S2 versus TOC were consistent with a weak to excellent hydrocarbon potential for the Laffan formation. On the other hand, organic petrographic techniques indicated that the primary organic constituents of the Laffan formation are inertinite and bituminite with subordinate amounts of amorphous organic matter (AOM). In other words, the contained organic matter was mainly composed of inertinite and lacked significant hydrocarbon potential. An abundance of inertinite and the conspicuous absence of vitrinite macerals in the studied samples suggested that the Laffan formation was deposited under sub-oxic marine conditions. Furthermore, the presence of bituminite in the studied samples greatly influenced the Rock-Eval pyrolysis readings, so geochemical evaluation of the Laffan formation using only Rock-Eval pyrolysis data may lead to erroneous interpretations. Therefore, a combination of Rock-Eval and organic petrographic methods is necessary for reliable geochemical evaluation of the Laffan formation. The results of this study can be useful for a better understanding of the Cretaceous hydrocarbon system in the study area.

Abstract Permeability is one of the most significant petrophysical parameters of reservoir rock, and its accurate, inexpensive, and rapid estimation is essential. One of the methods for the permeability analysis is the Stoneley flow zone index method. In this study, this method was used to estimate the permeability. For this purpose, after processing the Stoneley waves in the studied well by  Paradigm Geolog software, the permeability index was calculated based on Stoneley wave slowness. Then, by optimizing this index with default values of the flow zone index matching factor (IMF), the flow zone index was calculated, and the permeability value was estimated based on that index. Some parameters required for these calculations, such as porosity, type, and volume of minerals, were determined based on the complete set log analysis and with the help of cross-plots. Finally, these results were compared with the core data to validate the obtained permeability data, and the IMF values were customized for the studied field. The results indicated that the primary lithology of the Asmari formation in the studied well was carbonate rock with a small amount of shale. The customized IMF values for calcite, dolomite, anhydrite, and shale were 11.93, 10.53, 0, and 0, respectively. The correlation coefficient between Stoneley-flow zone index permeability and core permeability was 0.79. Therefore, according to this good correlation, this method can be used to estimate permeability, especially in wells without core data.

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. First, the highest possible correlation between the well logs and the seismic inversion data was established for this purpose. After extracting the optimal wavelet, an accurate relationship between the estimated values and the core data was obtained. Next, using data from another well, the validity of the constructed model was examined. The results showed that the combination of three attributes—instantaneous cosine of phase, , and —can accurately estimate the shale volume of the reservoir, with a correlation coefficient of approximately 70%. Although the two layers in the Ghar section have a shale volume of about 10%, the overall shale volume in the reservoir area is negligible. The logarithm of the ratio of compressional wave velocity to shear wave velocity attribute exhibits the highest correlation, approximately 62%. Finally, validation using previously unutilized well-log data demonstrated an accuracy of about 90% in predicting these properties.

Abstract The Qale-Nar oilfield is an asymmetric two-humped anticline located in the northernmost part of the Dezful embayment, in which the fractured Asmari carbonates are the primary reservoir rock. In this study, for the first time, the organic geochemistry of oils produced from the Asmari reservoir is used to investigate the reservoir continuity and possible compartmentalization. To this end, geological information from the studied oilfield was combined with bulk geochemistry (e.g., °API gravity) and molecular characteristics (e.g., gas chromatography (GC) and gas chromatography–mass spectrometry (GC–MS) data) of the produced oils. Two oil samples obtained from wells 6 and 10 of the studied oilfields indicate significant differences in their bulk and molecular geochemical properties. Accordingly, a scenario was presented to better explain the reservoir charging and compartmentalization in the Qale-Nar oilfield. In this scenario, low-maturity hydrocarbon pulses first charge the eastern culmination of the Qale-Nar oilfield. The activity of a fault plane located between wells 6 and 10 could induce a barrier between the two wells. Consequently, the late hydrocarbon charges with higher maturity could only charge the compartment belonging to well 6. Therefore, well 10 could not receive these high-maturity hydrocarbon pulses due to the lack of lateral connectivity. The information obtained from this study can be of great help in future reservoir studies with important implications for field development projects and enhanced-recovery plans.

Abstract The lithofacies and environments of deposition interpretations of the Calub–Hilala field toward the central trough of Ogaden Basin were analyzed, and geophysical well logs from three deep exploration wells, namely 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. An attempt was also made to identify formation tops and well-to-well lithostratigraphic correlation basing gamma-ray log trends and correlate with the cored interval of the wells for lithological comparisons. Lithofacies interpretation was carried out with Schlumberger’s Petrel software, version 2009. Correlation techniques were conducted to delineate the subsurface trends of these facies with electrofacies to compare facies interpretation results that were implied using the wireline log signatures.
Ten formations, namely Calub, Bokh, Gumburo, Adigrat, Transition, Hamanlei (Lower, Middle, and Upper), Urandab, Gebredare, Gorrahei, Mustahil, and five log facies, namely a cylindrical-shaped log trend representing aeolian, i.e., braded fluvial, a funnel-shaped facies representing a crevasse splay, a carbonate, shallowing upward sequence and shallow marine sheet sand, a 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.

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 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.

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 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.

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 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 (V_P/V_S) 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.

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 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 (Ø_N), density log (Ø_D), 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 (V_sh) by using gamma ray log and effective porosity (Ø_E) by using density and neutron logs. Resistivity of water (R_w) was calculated by SPmethod, and the saturation of water (S_w) and the saturation of hydrocarbons (S_h) 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: V_sh= 30%, Ø_E= 17%, S_w= 46%, and S_h= 54%. The analysis shows that S_h is low, so it is inferred that Lumshiwal formation has a low potential and is economically not feasible for hydrocarbons production.