Spectral Gamma Ray Research Articles

An improved permeability estimation model using integrated approach of hybrid machine learning technique and shapley additive explanation

Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production. Conventional methods relying on empirical correlations and assumptions often result in high costs, time consumption, inaccuracies, and uncertainties. This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield, Northwestern Uganda. The group method of data handling with differential evolution (GMDH-DE) algorithm was used to predict permeability due to its capability to manage complex, nonlinear relationships between variables, reduced computation time, and parameter optimization through evolutionary algorithms. Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing, the GMDH-DE outperformed the group method of data handling (GMDH) and random forest (RF) in predicting permeability with higher accuracy and lower computation time. The GMDH-DE achieved an R2 of 0.9985, RMSE of 3.157, MAE of 2.366, and ME of 0.001 during training, and for testing, the ME, MAE, RMSE, and R2 were 1.3508, 12.503, 21.3898, and 0.9534, respectively. Additionally, the GMDH-DE demonstrated a 41% reduction in processing time compared to GMDH and RF. The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin, Tanzania, which lacks core data. Shapley additive explanations (SHAP) analysis identified thermal neutron porosity (TNPH), effective porosity (PHIE), and spectral gamma-ray (SGR) as the most critical parameters in permeability prediction. Therefore, the GMDH-DE model offers a novel, efficient, and accurate approach for fast permeability prediction, enhancing hydrocarbon exploration and production.

Determination of clay minerals in Brunei's outcrops and spatial variability of the radioisotopes

Shale with the presence of clay minerals is one of the problematic formations to be encountered in the oil and gas industry. Drilling into these formations will be a challenge since shale sloughs and clay swells. Clay minerals impact hydrocarbon production and drilling operations due to clay hydration. Thus, the main objective of this paper is to identify the clay minerals from Brunei Darussalam outcrops based on the cross plot of thorium and potassium from the components of the radioisotopes of spectral gamma ray logging (SGRL). The majority of the outcrops in Brunei Darussalam contains montmorillonite which swell in contact with water. Geostatistical methods are implemented to gather spatial data over an area of interest. Variogram therefore is used to describe and characterize the variability of the radioisotopes by fitting a model to the experimental semi-variogram. The estimation of the measured values at any unknown location are then calculated by the kriging process. Semi variogram and corresponding kriging maps for thorium, uranium, and potassium components were prepared. The result indicated a good spatial similarity and areas with the highest number of the radioisotopes of spectral gamma ray logging presented the relationship of the radioisotopes in the surrounding area.

Outcrop spectral gamma-ray signals of distal arid continental basins: Facies identification and controls

Spectral gamma ray (SGR) studies can help link outcrop exposures to downhole data and provide useful case studies to reduce uncertainty in interpreting geophysical logs where core and other geological data is unavailable. However, this technique is underused in continental outcrop studies due to difficulties in determining facies comprising similarly sourced sediment, despite the fact that these deposits form prolific reservoirs for hydrocarbon and geothermal exploration, as well as carbon capture and hydrogen and storage. This work demonstrates the usefulness of spectral gamma ray measurements as an outcrop logging technique in determining quantifiable facies from the dryland continental sediments of the Cedar Mesa Sandstone Formation, Utah, USA. Complex sedimentary interactions between the aeolian, fluvial and lacustrine depositional environments are present, resulting in shared similar sedimentary characteristics within the preserved strata, such as mineralogy, grain size, sorting and provenance signals. Consequently, the geophysical signatures observed within the spectral gamma ray logs, have been suppressed, making it difficult to distinguish sedimentary facies from solely the spectral gamma ray responses. However, by combining K:Th cross-plot analysis and gamma ray log motif interpretation, this work demonstrates that individual facies within arid continental settings can be distinguished based on SGR outcrop logging, and unique spectral trends and values can be quantified and used to correlate facies across the depositional basin.

Study on quantitative interpretation of uranium spectral gamma-ray logging based on machine learning algorithm

Spectral gamma-ray logging stands out as an efficacious methodology in the exploration of uranium(U). In order to address the challenges associated with low statistical efficacy in spectral analysis and mitigate the impact of spectral drift, machine learning algorithms such as Back Propagation (BP) neural network, Generalized Regression Neural Network (GRNN), and Support Vector Machine (SVM) have been employed for the quantitative interpretation of uranium. A method rooted in machine learning algorithms for energy spectral analysis has been proposed, catering to the analysis of high-speed spectra logging and gamma-ray spectra characterized by spectral drift. Examinations conducted on standard model wells containing uranium revealed that the BP neural network exhibited commendable accuracy in uranium interpretation, achieving quantification accuracy rates of 86.986 % and 93.478 % for low-grade and medium-high-grade uranium ores in the Testing Set, respectively. Notably, marginal variations in the model's quantification errors were observed under diverse logging speeds and spectral drift conditions, underscoring the capacity of gamma-ray spectral quantitative interpretation through machine learning algorithms to effectively surmount the impacts of logging speed and spectral drift. This machine learning-based approach to energy spectral analysis proved instrumental in enhancing traditional logging speeds to 6 m/min, presenting a novel perspective for the quantitative interpretation of uranium spectral gamma-ray logging at heightened logging speeds.

Penentuan Nilai Porositas pada Sample Berea di Routine Core Analysis Laboratorium BBPMGB “LEMIGAS”

This study aims to analyze routine cores, especially for determining the porosity of a rock. The samples tested by the coreval tool were 3 samples of 1.5 inch diameter with a length of 4.5 cm each. The sample used is a sample of Berea sandstone. When the core sample comes, it is continued with ct-scan and spectral gamma ray. The next process is washing the fluid in the core sample. After cleaning from other fluids, the core sample will be dried in the oven. After it is completely dry, the sample must be stored in a desiccator containing silica gel. After ambient temperature, then the core sample is tested using Coreval 700. The data inputted into the coreval computer are the name of the core sample, the dry weight of the core, the length of the core, and the diameter of the core. The coreval tool takes readings of bulk volume, pore volume, and porosity. This reading resulted in bulk volume values of 50.091 cc; 53.446 cc; and 52.410 cc, respectively. The pore volume value of each sample is 12.091 cc; 13.787 cc; and 13.385 cc. As for porosity, the value of each sample is 25.767%; 25.795%; and 25.540%. Keywords: coreval 700; permeability; porosity, RCAL

In-situ radiogenic heat production in the Cenozoic sediments of the North sea

Radioactive decay is a significant heat source in sedimentary basins. Spectral gamma ray logs from the North Sea well 1/2-2 were used to obtain the uranium, thorium and potassium concentrations in the Cenozoic sediments beneath the well. On average, the sediments contain 1.86 ± 0.37% potassium, 10.12 ± 2.55 ppm thorium and 2.74 ± 1.54 ppm uranium. Abnormally high, up to 8 ppm, uranium concentrations were measured in the transition zones between the Nordland and the Hordaland Group and in the Sele Formation of the Rogaland Group.The estimated heat production rate, of all in-situ radioactive isotopes, is, on average, 1.281 ± 0.35 μW/m3. About 12% (0.143 ± 0.31 μW/m3) can be contributed to potassium, 46% (0.570 ± 0.14 μW/m3) to thorium and 42% (0.567 ± 0.31 μW/m3) to uranium. The estimated radiogenic heat rates vary significantly across the main stratigraphic units. On average, radiogenic heat production is more intensive in shale-dominated material (1.33 ± 0.31 μW/m3) than in sand-rich sediments (0.84 ± 0.36 μW/m3). High heat production rates are very closely related to high uranium concentration. In the “hottest” (2.012 ± 0.27 μW/m3) unit, the Sele Formation, the uranium accounts for about 69% of the generated heat, while the thorium and the potassium contribute with 24% and less than 7%, respectively. In the ”coldest” (0.782 ± 0.35 μW/m3) unit, the Forties, the relative contribution of uranium is only about 36% and that of potassium and thorium are about 18% and 46%, respectively.The radiogenic heat rates in the Cenozoic sediments should be integrated in future evaluations of the geothermal potential in the North Sea.

Forward modeling of spectral gamma-ray (SGR) logging in sedimentary formations

We propose a new approach to improve spectral gamma-ray (SGR) logging forward modeling by considering the radioactive minerals present in the rock as gamma-ray sources. This is based on the radioactive attenuation theory. The assumptions are: 1) minerals with K40, U238, and Th232 content are considered radioactive sources uniformly distributed in the rock; 2) the measured radioactivity is proportional to the concentration of radioactive minerals and inversely proportional to the rock bulk density; 3) the radioactivity is only attenuated by absorption of gamma-rays. The forward modeling was tested using a synthetic case of sandstone with clay minerals and brine-saturated pores to analyze the sensitivity of SGR to changes in illite/smectite-illite/mica ratios and sandstone porosities. Finally, it was further validated with 44 core samples, of which 22 are from two shale gas and 22 from two clastic formations. The Pearson correlation coefficient applied to measure the misfit between the simulated and observed K, U, Th, and SGR data attained values of 0.82, 0.83, 0.61, and 0.57, respectively. A further improvement to 0.87, 0.85, 0.65, and 0.69 was achieved when applying joint inversion for data where illite/smectite and illite/mica ratios were not specified. The correlation between the simulated and observed data supports the viability of the proposed SGR forward modeling approach method.

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks.

An innovative approach for investigation of overpressure due to hydrocarbon generation: a regional study on Kazhdumi formation, South-western Zagros Basin, Iran

Overpressure is always considered as a severe problem in the oil industry. Besides creating life risks through serious accidents while drilling, failure to correctly identify high-pressure intervals causes a significant increase in costs and prolongs the drilling process. Kazhdumi Formation in the Abadan Plain is considered as a high-pressure formation in several wells. Various reasons can cause overpressure problem. Hydrocarbon generation is one cause of abnormal pressure in source rocks. Understanding hydrocarbon generation potential can be a helpful approach since the Kazhdumi Formation is considered as a probable source rock in this area. In this paper, in order to better understand the problem of abnormal pressure in Kazhdumi Formation, geochemical concepts and tools have been applied. To that way, 1D petroleum system modeling of five wells was done, and the thermal maturity level of Kazhdumi Formation was determined and then, compared with drilling records. The results indicate that in wells where this formation has sufficient organic matter and has reached an early mature level, there is an abnormal pressure problem. Otherwise, this formation does not show abnormal pressure. Also, geochemical data are not available in all drilled wells, which makes impossible the assessing of hydrocarbon generation role. Therefore, petrophysical well logs (sonic (DT), neutron (NPHI), density (RHOB), spectral gamma ray (SGR), and resistivity (RES)) as well as 137 sets of geochemical data belonging to 13 wells from 7 oilfields in the Abadan Plain were used to predict geochemical indicators. Using artificial neural networks, geochemical data of a well in Abadan Plain were predicted. This selected well has a high-pressure problem in Kazhdumi Formation, but no geochemical data are available in this well. The results of predicted geochemical data show that the high-pressure phenomenon in this well may also be due to hydrocarbon generation. The precise understanding of the abnormal pressure, resulting from hydrocarbons generation, requires comprehensive studies and a full investigation of the studied area. However, the results of this paper help to predict approximately the behavior of the source rocks before drilling. Acquiring this overview will aid in reducing drilling hazards and costs.

Paleoenvironmental reconstruction of the Berriasian organic-rich interval of the Vaca Muerta Formation (Neuquén Basin, Argentina): Insights for the characterization of unconventional hydrocarbon shale reservoirs

The 50 m-thick Berriasian organic-rich interval (BORI) of the Vaca Muerta Formation (Neuquén Basin, Argentina) represents one of the most attractive stratigraphic intervals for unconventional hydrocarbon exploration in the unit. Nevertheless, little is known about the environmental conditions prevailing during the accumulation of the sediments. To unravel the causes that favored the preservation of the organic matter (OM) in the BORI, a detailed multiproxy sedimentological approach was conducted. The study included the analyses of microfacies, palynological content, calpionellid-calcisphere dinocysts, mineralogy, spectral gamma-ray, inorganic geochemistry, Rock-Eval pyrolysis and stable carbon isotopes of whole rock carbonate (δ13Ccarb), and associated OM (δ13Corg). The BORI presents a late Early to early Late Berriasian age and is subdivided into a lower and upper interval. The lower interval (25.6 m-thick) is primarily formed by radiolaritic wackestones and presents higher total organic carbon (TOC) content (4.2 wt% on average), mainly in the form of amorphous OM (AOM), with variable contribution of phytoclasts. The geochemical analyses (Si, Ni, Cu, Mo, U, V) indicate high productivity of the water column and overall sea bottom anoxia. Oppositely, the upper interval (24.4 m-thick) is mostly constituted by peloidal packstones/grainstones and presents a lower TOC content (1.5 wt% on average) mainly constituted by phytoclasts, with variable contribution of AOM. A decrease of the productivity and an increase of sea bottom oxygenation is recorded based on the geochemical analyses. The high OM content of the BORI responds to the combination of high productivity of the water column and sea bottom anoxia in response to the Berriasian transgression and a worldwide paleoclimatic change towards more humid conditions. The results of our study position the BORI as an attractive interval for unconventional hydrocarbon production due to its high TOC, low bulk clay mineral and high biogenic quartz content, granting an adequate geomechanical behavior.

A Paleogene mixed carbonate-siliciclastic system in the western Tethys: spectral gamma-ray as a tool for the reconstruction of paleoclimate and transgressive-regressive cycles

The mixed carbonate-siliciclastic ramps of the Dauphinois-Provençal domain in northwest Italy are characterized by a common development process during the Paleogene. Spectral gamma-ray (SGR) analysis indicates the concentration of potassium (K), uranium (U), and thorium (Th) in the total gamma-ray signal, permitting the identification of subtle stratigraphic compositional trends, sedimentary minerals, paleoclimatic conditions, and the individualization of lithofacies characters through the stacking pattern cycles. This study integrates known lithological and fossil content analysis of the section with new independent data from the spectral gamma-ray (SGR) to explore the paleoclimate and stacking patterns for defining large-scale depositional cycles along a Paleogene succession in the southwestern Alps.The differences between the outcropping Microcodium Formation and the Capo Mortola Calcarenite Formation are indicated by the SGR, conventionally shown on a linear scale as American Petroleum Institute (API) units, i.e., Th/U, and Th/K ratios. Our data indicate lithofacies associated with normal marine to continental conditions and the presence of abundant smectite, rather than kaolinite, suggesting that the prevailing climate was characterized by prolonged dry periods. Furthermore, the SGR (API) reconstruction of the overall short cycles reflects a progradational and/or retrogradational stacking geometry that accumulated throughout the whole succession. However, the use of the derivative trend analysis (DTA) of the SGR indicates the establishment of long cycles in both formations. The short cycles observed in the Capo Mortola Calcarenite Formation are of particular importance because they characterize the marine transgression of the middle-upper Eocene, transgressive upon the Upper Cretaceous beds, and permit pinpointing the onset of the drowning ramp, which can be correlated regionally with sections spanning from the French-Italian Maritime Alps to the France-Switzerland border.

Identification of carbonate sedimentary facies from well logs with machine learning

Sedimentary facies identification is critical for carbonate oil and gas reservoir development. The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time. Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers' subjective influence. Although many references reported the application of machine learning to identify lithofacies, but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement. This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models, and the optimal machine learning with the highest prediction accuracy is recommended. First, the carbonate sedimentary facies are classified into the lagoon, shallow sea, shoal, fore-shoal, and inter-shoal five tags based on the well loggings. Then, five well log curves including spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), true formation resistivity (RT), shallow lateral resistivity (RS) are used as the input, and the manual identified carbonate sedimentary facies are used as the output of the machine learning model. The performance of four different machine learning algorithms, including support vector machine (SVM), deep neural network (DNN), long short-term memory (LSTM) network, and random forest (RF) are compared. The other two wells are used for model validation. The research results show that the RF method has the highest accuracy of sedimentary facies prediction, and the average prediction accuracy is 78.81%; the average accuracy of sedimentary facies prediction using SVM is 77.93%. The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM, and the average accuracy is 69.94% and 73.05%, respectively. The predicted carbonate sedimentary facies by LSTM are more continuous compared with other machine learning models. This study is helpful for identifying compelx sedimentary facies of carbonate reservoirs from well logs.

Spectral Gamma Ray Log-Based Shale Volume Estimation of a Gas Well, Bengal Basin

Background: Formation evaluation plays a pivotal role in identifying lithology and its depth of occurrence in gas fields. This study focuses on the Bengal Basin, aiming to achieve lithology identification and shale volume estimation in a gas well. Method: The gamma ray (GR) log is utilized to measure natural radioactivity, with spectral gamma ray (SGR) employed to capture concentrations of potassium, uranium, and thorium in clastic sedimentary formations. Lithology identification is conducted using resistivity, SGR, and GR logs, while shale volume estimation utilizes standard models including GR and true resistivity approaches. Results: The lithology of the studied well predominantly comprises clastic sedimentary rocks, specifically sand and shale. The reservoir rock type is primarily sandstone, with sand being the dominant fraction and shales appearing laminated. Concentrations of thorium, uranium, potassium, and gamma ray are approximately 12.46 ppm, 2.28 ppm, 1.73%, and 100 API, respectively. The shale volume of the gas reservoir ranges from 12% to 29%. Conclusion: The estimated shale volume provides valuable insights for assessing effective porosity and hydrocarbon saturation in shaly sand reservoirs, facilitating gas resource estimation and reservoir characterization in the sedimentary basin. This study underscores the significance of comprehensive formation evaluation techniques in optimizing reservoir management and resource extraction strategies.

Geochemical evaluation and source rock zonation by multi-layer perceptron neural network technique: a case study for Pabdeh and Gurpi Formations-North Dezful Embayment (SW Iran)

In this study, using a multi-layer perceptron neural network (MLPNN) model, total organic carbon (TOC) and hydrogen index (HI) values for Pabdeh and Gurpi Formations in the oil fields of Naft Sefid (NS-13), Kupal (KL-36, KL-38, and KL-48) and Palangan (PL-2) were calculated in the North Dezful Embayment located in the southwest of Iran. To build the MLPNN model, the geochemical data calculated by the Rock–Eval pyrolysis method (TOC and HI) and the conventional petrophysical well log data, including sonic transit time log (DT), formation density log (RHOB), total resistivity log (RT), spectral gamma-ray log, computed gamma-ray log and neutron porosity log from the NS-13 well were used. The log data were the input layer, and the geochemical data were the output layer of the model. Twenty-four datasets were used for MLPNN training, and seven datasets were used for MLPNN testing. Two hidden layers were considered in this technique. Each hidden layer has an activation function (tanh) and a solver parameter (lbfgs). The accuracy of measurement of TOC and HI indices of Pabdeh and Gurpi Formations in terms of R2 was 0.93 and 0.90, respectively. This model has higher accuracy than the ΔlogR technique (R2: 0.28). Considering the relationships between the input data and other wireline logs is an advantage of this technique. These two formations have five source rock zones. Pabdeh Formation has three zones. The middle zone of the Pabdeh Formation (Pz. II) has the highest TOC (2.6 wt%) and source rock potential. Pabdeh Formation has kerogen type II. Gurpi Formation has a weaker source rock potential than Pabdeh Formation due to its low TOC content (< 1%). Both source rock zones of this formation have low TOC, but in some layers of the lower zone of the Gurpi Formation (Gz. II), high values for TOC were predicted. Gurpi Formation has Kerogen types II and III.

Evaluation of the Shale Impact on Reservoir Characterization, the Jeribe Carbonate Reservoir in an Oilfield Northern Iraq as a Case Study

The shale content and mode of distribution, along with their impact on the reservoir properties of the Jeribe Formation, were investigated using the available log data in the two selected wells, NET-10 and NET-12, of an oilfield northern Iraq. The dolomite and dolomitic limestone lithology of the formation contains different ratios of shale, with the highest near the middle part of the formation. Horizons of 70 to 99% shale content were identified, but the general ratios are ranging between 10% and 50%. The data from the Spectral Gamma ray log revealed that the shale content of the formation is mostly composed of low Potassium minerals such as Chlorite and Montimorlinite, with appreciable percentages of Illite. The low Th/U ratios along the formation indicated a reduced condition of deposition except for about 2-3 meters of the upper part of the formation in the well NET-10, which looks to be precipitated in a natural depositional environment. Dispersed, Laminar, and Structural modes of shale distribution co-exist within the formation in both studied wells. As the different modes of shale distribution have different impacts on the porosity and permeability of the reservoir rocks, the decrease and increase in the shale content did not perfectly correspond with an opposite fluctuation in the porosity values of the formation. The shale content in the formation has an impact on the porosity calculation by overestimating it by about 4–5% and subsequently underestimating the water saturation by 9% in the well NET-10 and 7% in the well NET-12.

Thermal structure of two deep boreholes in the Siljan Ring, Central Sweden, and palaeoclimatic implications

Heat-flow data from deep boreholes are of particular value as a number of factors, including climatic surface temperature variations, may disturb subsurface thermal conditions, especially at shallow depths. This study provides thermal results from two boreholes, Gravberg-1 and Stenberg-1, drilled to large depths in the Siljan Ring impact structure, Baltic Shield, Central Sweden. Taking several high-resolution temperature logs to a depth of more than 5000 m in Gravberg-1 and close to 2000 m in Stenberg-1, and up to a long time after drilling, ensures high-quality deep temperature data. A good-quality thermal conductivity profile was determined from mineralogical composition and radiogenic heat production from a spectral gamma-ray log. The observations show overall thermal conductive profiles with local non-conductive temperature variations. Observed present-day near-surface heat flow (depth range 200–500 m) in Gravberg-1 is 47 mW/m2 (std. dev. 1.5 mW/m2). From observed deep background heat flow, considering heat production, the equivalent steady-state, unperturbed heat flow is estimated at 66–68 mW/m2. Data from Stenberg-1 provide similar results. The observed reduction of heat flow at near-surface level by about 20 mW/m2 is interpreted as originating from long-term palaeoclimatic temperature variations. During the last glacial period, the area was, to varying degree, both ice-free and covered by the Fennoscandian ice sheet. Thermal modelling, with a ground-surface mean-temperature increase by 12±2 °C from glacial to post-glacial time, shows consistency between observed and modelled heat flow perturbations for the full depth range of observations. Heat flow perturbations may be significant to depths of about 2000 m. These results show that, uncorrected for palaeoclimate, depending on depths of measurements, observed continental heat flow values might be markedly underestimated. This applies to Northern Europe and, likely, to many other areas as well.

Gamma-ray, stable carbon and oxygen isotope chemostratigraphy and sequence stratigraphy of the Lower Mahil Formation (KS-1 Khuff-Equivalent), Northern Oman

This research presents findings from a study focused on the Lower Triassic (Induan) Lower Mahil KS-1 Formation, situated on a homoclinal carbonate platform in Northern Oman. The sequence stratigraphy of this formation is characterized by a considerable thickness variation, slumps, and breccia deposits related to active normal faults coupled with intra-basin growth faults. The main objective was to establish a reliable stratigraphic framework for the Lower Mahil KS-1 Formation by integrating high-resolution carbon isotope data along with high-resolution spectral and total gamma-ray data. To achieve this, whole-rock samples were analyzed for δ13C and δ18O isotopes. Spectral and total gamma-ray records were obtained for the formation. Isotope sampling is conducted every 0.5 m in the Saiq Plateau and Wadi Sahtan sections. Furthermore, spectral gamma-ray measurements were taken at intervals of 10 cm from the logged sections. Within the third-order sequence, the spectral gamma-ray data revealed a distinct sea-level trend, leading to the division of KS1 into two different parts. Five fourth-order depositional sequences were identified by analyzing stable carbon isotopes, uranium, and total gamma-ray profiles. Four of these sequences displayed complete patterns, reflecting transgression and regression phases, while the fifth sequence was incomplete and solely comprised a transgressive phase. An essential outcome of the study is the correlation of the δ13C curve of the Lower Mahil KS-1 Formation with other similar formations around the Tethys region. This correlation indicates that the Lower Mahil KS-1 Formation captures the near-primary signal of carbon isotope variations in coeval seawater. As a result, it holds promise as a reference section for future investigations and studies in this field. Compared to the prior investigation, this study utilizes data with higher precision, capturing spectral gamma-ray measurements at 10 cm intervals and isotope measurements at 50 cm intervals. Furthermore, the study’s focus is confined explicitly to KS1.

Evolution of Casedhole Nuclear Surveillance Logging Through Time

Nuclear logging techniques have played a critical role in the evaluation and surveillance of hydrocarbon reservoirs since the introduction of the gamma ray log in 1939. This paper reviews the history of key developments in nuclear logging that led to improved methods to identify gas-oil (GOC), gas-water (GWC), and oil-water (OWC) contacts in steel-cased wells, as well as methods to identify gas, steam, and waterflood front encroachment, calculate their saturations, and recognize problems in efficient reservoir depletion. This paper will focus solely on nuclear methods used to directly identify fluids behind pipe using natural gamma radiation, neutron-induced gamma radiation, and neutron flux measurements. This includes gamma ray, spectral gamma ray, single- and dual-detector neutron measurements, pulsed-neutron capture, (), and pulsed-neutron spectroscopy (carbon/oxygen or C/O) methods. It will not cover other methods of identifying fluids behind pipe, such as borehole gravity and deep electromagnetic (EM) methods using wired pipe. It also will not cover indirect methods to infer fluid types in reservoirs, such as nuclear production logging using gamma density and pulsed-neutron measurements.

Detection of potential hydrocarbon accumulations at Qaret El-Soda area, Western Desert, Egypt, based on airborne geophysical survey data

Airborne spectral gamma-ray survey data were processed using Th-normalization technique for oil and gas exploration in the Qaret El-Soda area, Western Desert of Egypt. This technique was applied to suppress the effects of surface lithology, which are the main factors influencing the variation of radioelement content in rocks. Normalization of K and U by thorium yielded residual potassium and residual uranium estimates. Possible occurrences of new hydrocarbon microseepages were determined by mapping low values of residual potassium and high values of residual uranium relative to potassium, which are indicated as DRAD values, which were obtained by subtracting residual potassium from residual uranium values (eUresid – Kresid). Lower residual values of K, which were associated with higher DRAD anomaly values, highlight areas of prospective hydrocarbon accumulations. The obtained results from quantitative analysis and interpretation of aeromagnetic data show sufficiently thick sediments, probably suitable for the accumulation of hydrocarbons. This means that the study area may possess a potential for hydrocarbon exploration if supported by other detailed geophysical and geochemical exploration techniques.

Petrophysical characterization of the heterogeneous shale-rich oil reservoirs: a case study of the Cenomanian Clastics, Abu Sennan Concession, North Western Desert of Egypt

Shale-rich reservoirs present a long-standing challenge for reservoir geologists because the clay minerals often induce a large-scale heterogeneity in the reservoir pore system. This work aims to understand the impact of clay distribution and mineralogy which would enhance the predictability of the best reservoir facies. We integrate seismic, well-log datasets to investigate the petrophysical characteristics of the clay-rich Cenomanian Clastics in the GPY oil field, north Western Desert of Egypt. These Clastics comprise the sandstone intervals which are the most prolific hydrocarbon reservoirs. Seismic data were used to interpret the main structural patterns as well as the different seismic facies. The well log data were utilized to interpret the lithologic variations and the type of clays in the reservoir as well as the different petrophysical parameters. Based on variations in their lithological and petrophysical characteristics, the Bahariya sandstones were sub-divided into three different rock units: Bahariya-3 (B-3), Bahariya-2 (B-2), and Bahariya-1 (B-1), separated by thick laminated clay intervals. AR/G Member is dominated by clays with relatively lower reservoir quality. Spectral gamma ray log values reveal that smectite is the dominant clay mineral in all the studied intervals. Laminated clays are dominant in B-1 and B-2 units, whereas, B-3 unit and Abu Roash G Member are enriched in structural clays. The quartzose sand content decreases from B-3 to AR/G and clay content increases from B-3 to AR/G. Therefore, the best reservoir facies and fluid flow conduits with best pore system characteristics are hosted in B-3 and the smectite clay streaks act as a good seal for hydrocarbons in the quartzose sandstone pay zone.