RHOB Logs Research Articles

Evaluation of Gas Potential in Early Cretaceous Shale Lower Indus Basin, Pakistan

The shale gas potential of the early Cretaceous Talhar shale member of the lower Goru Formation in lower Indus basin of Sanghar district, Sindh province, has been evaluated using 2D seismic and well logs data. Graphical and empirical techniques were applied to calculate various geochemical parameters for the evaluation of shale plays. Total Organic Carbon (TOC) is calculated by Passey's (∆LogR) Overlay technique and its values are about 2.44 wt.%. Vitrinite Reflectance (Ro) is estimated by graphical and empirical relation and the value lies between 0.95-1.0 which implies that the Talhar shale member is in peak oil and initial gas generation phase. Log-derived Maturity Index (LMI) is calculated by NPHI, RHOB or density log and uranium logs. The average value is about 0.55 which reflects that the formation is probably in the initial maturation phase. Kerogen Volume (Vk) is estimated by empirical relation using RHOB log as input and it is about 13 wt. % which reflects that the formation has enough potential of oil/gas expulsion. The results are compared and validated with a study of the adjacent field of the same basin. This study reveals that Talhar member of lower Goru Formation could be the future probable potential unconventional reservoir for exploration in lower Indus basin of Pakistan.

Evaluation of Gas Potential in Early Cretaceous Shale Lower Indus Basin, Pakistan

The shale gas potential of the early Cretaceous Talhar shale member of the lower Goru Formation in lower Indus basin of Sanghar district, Sindh province, has been evaluated using 2D seismic and well logs data. Graphical and empirical techniques were applied to calculate various geochemical parameters for the evaluation of shale plays. Total Organic Carbon (TOC) is calculated by Passey's (∆LogR) Overlay technique and its values are about 2.44 wt.%. Vitrinite Reflectance (Ro) is estimated by graphical and empirical relation and the value lies between 0.95-1.0 which implies that the Talhar shale member is in peak oil and initial gas generation phase. Log-derived Maturity Index (LMI) is calculated by NPHI, RHOB or density log and uranium logs. The average value is about 0.55 which reflects that the formation is probably in the initial maturation phase. Kerogen Volume (Vk) is estimated by empirical relation using RHOB log as input and it is about 13 wt. % which reflects that the formation has enough potential of oil/gas expulsion. The results are compared and validated with a study of the adjacent field of the same basin. This study reveals that Talhar member of lower Goru Formation could be the future probable potential unconventional reservoir for exploration in lower Indus basin of Pakistan.

Evaluation of Gas Potential in Early Cretaceous Shale Lower Indus Basin, Pakistan

The shale gas potential of the early Cretaceous Talhar shale member of the lower Goru Formation in lower Indus basin of Sanghar district, Sindh province, has been evaluated using 2D seismic and well logs data. Graphical and empirical techniques were applied to calculate various geochemical parameters for the evaluation of shale plays. Total Organic Carbon (TOC) is calculated by Passey's (∆LogR) Overlay technique and its values are about 2.44 wt.%. Vitrinite Reflectance (Ro) is estimated by graphical and empirical relation and the value lies between 0.95-1.0 which implies that the Talhar shale member is in peak oil and initial gas generation phase. Log-derived Maturity Index (LMI) is calculated by NPHI, RHOB or density log and uranium logs. The average value is about 0.55 which reflects that the formation is probably in the initial maturation phase. Kerogen Volume (Vk) is estimated by empirical relation using RHOB log as input and it is about 13 wt. % which reflects that the formation has enough potential of oil/gas expulsion. The results are compared and validated with a study of the adjacent field of the same basin. This study reveals that Talhar member of lower Goru Formation could be the future probable potential unconventional reservoir for exploration in lower Indus basin of Pakistan.

Analysis of Physical Properties of Rock Reservoirs Using Well Log Method in Determining Reservoir Quality in “JEONA” Field, Central Sumatra Basin

The industrial revolution 4.0 has required collaboration on the oil and gas industry sector. One of the problems is the technology transfer and the increasing of oil reserves supply. It needed to productivity analysis for reservoir rock characterization and subsurface profile that will determine technology transfer. This study was aimed to obtain the hydrocarbon reservoirs quality based on petrophysical parameters. The reservoir zone of JEONA_35 well in 1527.77 - 2067.21 ft. From NPHI and RHOB logs, there is a cross over in 1990-2020 ft that interpreted as oil fluid. The PHIE is 30 %, Sw is 20 % and Vclay is 10 %. Electrofacies analysis shows that the reservoir zone was located in Bekasap and Menggala Formations (1527.77 - 1889.25 ft). In Bekasap Formation, the gamma ray log pattern is a cylinder block shape and thick from the top until base formation, it deposited in the channel fill distribution with thin sandstones lithology. In Menggala Formation, gamma ray log pattern shows the uptrend bell shape that was deposited in the fluvial - delta distribution with thick sandstone. So, it can be concluded that JEONA_35 reservoir has a good quality with the water saturation is low relatively, so the oil saturation is high relatively.

Delineation of hydrocarbon and non-hydrocarbon zones using fractal analysis of well-log data from Bhogpara oil field, NE India

Hydrocarbon (HC) and non-hydrocarbon (NHC) zones have been characterized using fractal analysis of wireline log data. Well-log responses such as spontaneous potential (SP), gamma ray (GR), resistivity (LLD, LLS, MSFL), neutron porosity (NPHI), bulk density (RHOB) log have been analyzed from five boreholes of Bhogpara oil field using rescaled range (R/S) and power spectrum density (PSD) techniques. Primarily, both the techniques were applied on the log responses associated with the traditionally identified potential and non-potential zones of 100 m thickness. The Hurst exponent (HPSD) obtained using PSD analysis for GR, NPHI and RHOB log, shows higher values for HC zones as compared to the NHC zones. Furthermore, to understand the scaling pattern more explicitly for main potential sector of the reservoir. Both of the techniques were applied to the wireline log data that are associated with traditionally defined HC and NHC zones of different thickness. The Hurst exponent (HR/S) obtained using R/S analysis was found to be of relatively higher values for HC zones as compared to the NHC zones for all wireline log response. Again opposite trend was also observed for HPSD values that obtained from GR, MSFL, NPHI and RHOB log responses only. Hence, the HC and NHC zones can be delineated from fractal analysis of wireline logs and also overcome the chance of misinterpretation, which is quite possible in the case of hydrocarbon zone detection in traditional reservoir characterization.

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

Synthetic well log generation using artificial intelligence tools is a robust solution for situations in which logging data are not available or are partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. These data are measured in the field while drilling by using a density log tool in the form of either a logging while drilling (LWD) technique or (more often) by wireline logging after the formations are drilled. This is due to operational limitations during the drilling process. Therefore, the objective of this study was to develop a predictive tool for estimating RHOB while drilling using an adaptive network-based fuzzy interference system (ANFIS), functional network (FN), and support vector machine (SVM). The proposed model uses the mechanical drilling constraints as feeding input parameters, and the conventional RHOB log data as an output parameter. These mechanical drilling parameters are usually measured while drilling, and their responses vary with different formations. A dataset of 2400 actual datapoints, obtained from a horizontal well in the Middle East, were used to build the proposed models. The obtained dataset was divided into a 70/30 ratio for model training and testing, respectively. The optimized ANFIS-based model outperformed the FN- and SVM-based models with a correlation coefficient (R) of 0.93, and average absolute percentage error (AAPE) of 0.81% between the predicted and measured RHOB values. These results demonstrate the reliability of the developed ANFIS model for predicting RHOB while drilling, based on the mechanical drilling parameters. Subsequently, the ANFIS-based model was validated using unseen data from another well within the same field. The validation process yielded an AAPE of 0.97% between the predicted and actual RHOB values, which confirmed the robustness of the developed model as an effective predictive tool for RHOB.

Sedimentary units-layering system and depositional model of the carbonate Mishrif reservoir in Rumaila oilfield, Southern Iraq

The Mishrif Formation (Cenomanian–Lower Campanian) is the main productive oilfield at Basrah in southern Iraq. It mainly comprises of shales and carbonates characterized by vertical phase transitions, including seals and oil reservoirs. This study establishes the Cenomanian–Campanian sequence stratigraphic framework through the integration of geochemical and geophysical data in order to distinguish between the stratigraphic intervals of shale and non-shale along oil wells in the Rumaila Oil Field that penetrates the Mishrif, Khasib and Tanuma formations. The geochemical profile has shown that carbonate and shale are the main lithologies along the 315 m stratigraphic interval from the top of Tanuma to the base of Mishrif, in the oil well WQ2-139. The stratigraphic column accordingly was divided into twenty stratigraphic intervals (zones); four zones in Tanuma, five zones in Khasib and eleven zones in the Mishrif Formation. The lower part of the Mishrif Formation is the main reservoir in the stratigraphic column as already known, yet the problem lies in the controversy over the number of productive units and their stratigraphic intervals. The porosity, lithology and fluid characters were discriminated based on distribution temperature (DT), Distribution Temperature Sensing (DTS) and RHOB logs as well as the compressional-wave velocity (Vp), shear-wave velocity (Vs) and density logs. The shale, gas limestone, oil limestone and brine limestone are well estimated and presented where the chi angle is about 0° and fluid-fill (i.e. oil from brine) is 30°–50°. This study provided headway in establishing milestones in the stratigraphic framework that have an important effect in determining reservoir storage quality.

Fast fuzzy modeling method to estimate missing logsin hydrocarbon reservoirs

Abstract In order to deal with huge amounts of computational complexities, conventional modeling systems always had to choose a tradeoff between accuracy and rapidity and usually one prevails over the other. Thus there is a need for a solution which provides acceptable accuracy and rapidity at the same time. In this research we propose a new fast fuzzy modeling method (FFMM) using Ink Drop Spread (IDS) and Center of Gravity (COG) operators. We applied this method to estimate missing logs of sonic and density. In the petroleum industry, characterization of pore–fluid pressures and rock lithology, along with estimation of porosity, permeability, fluid saturation and other physical properties are crucially important for successful exploration and exploitation. For many reasons, such as incomplete logging, inappropriate data storage and measurement errors, log suites are either incomplete or unreliable. By applying the proposed method, we estimated sonic and density logs. Correlation coefficients and MSEs for DT and RHOB logs were equal to 0.92, 21.07 and 0.85, 0.006 respectively. These results show that, despite the algorithm's very fast computation speed, its performance is comparable with that of methods like artificial neural network (ANN) and conventional Fuzzy Logic (FL); but the latter requires large amounts of storage and computing time, while the resources needed for the proposed method are limited to fixed values with respect to the number of data points.

Analysis of Cross Correlations Between Well Logs of Hydrocarbon Reservoirs

We carry out a series of cross-correlation analysis of raw well-log data, in order to study the possible connection between natural gamma ray (GR) logs and other types of well logs, such as neutron porosity (NPHI), sonic transient time (denoted usually by DT), and bulk density (RHOB) of oil and gas reservoirs. Three distinct, but complementary, methods are used to analyze the cross correlations, namely, the multifractal detrended cross-correlation analysis (MF-DXA), the so-called Qcc(m) test in conjunction with the statistical test—the χ2(m) distribution—and the cross-wavelet transform (XWT) and wavelet coherency. The Qcc(m) test and MF-DXA are used to identify and quantify the strength of long-range cross correlations between the porosities derived based on the NPHI, DT, and RHOB logs on the one hand, and the GR log that is indicative of the presence of clay minerals in reservoir rocks, on the other hand. The Qcc(m) test describes qualitatively the presence of such cross correlations between the porosity and GR logs. Analysis by the MF-DXA method also indicates that the various porosity logs, the GR log, and the cross correlations between them are multifractal, hence confirming the long-range nature of the correlations. The results are confirmed further by the XWT, and indicate that the porosities estimated based on the NPHI logs are only weakly, if at all, affected by the natural GR radioactivity of reservoir rock and are, therefore, most reliable. The effect of length scale on the correlations and cross correlations was studied in detail. It is shown that such correlations exist at all length scales, and that they are of multifractal type that must be characterized by a spectrum of exponents.