Gamma-ray Logs Research Articles

Exploring Variations in Orientation and Velocity of Hydraulic Fracture Propagation in Denver-Julesburg Basin

Summary This research explores the variations in hydraulic fracture (HF) propagation orientation and velocity within a zipper-fractured area of the Denver-Julesburg (DJ) Basin. By using a comprehensive analytical approach that integrates crosswell low-frequency distributed acoustic sensing (LF-DAS) strain signals, 3D seismic images, borehole image logs, gamma ray (GR) logs, and zipper-fracturing completion sequences, the study examines the dynamic responses of HF propagation under various geological and stimulation conditions. A detailed field case study was conducted across seven horizontal wells, with the entire hydraulic fracturing process monitored through fiber optics installed in two offset horizontal wells. Results from this study reveal significant variations in HF propagation behavior, likely due to pre-existing natural fractures, stress anisotropy, completion sequence, and fault impacts. These findings contribute to a better understanding of fracture mechanics in unconventional reservoirs, thereby informing more effective hydraulic fracturing strategies. This work ultimately provides crucial insights that can guide future optimization of hydraulic fracturing techniques in complex geological settings within and beyond the DJ Basin.

Predictive pore pressure modeling using well-log data in the West Baram Delta, offshore Sarawak Basin, Malaysia

Accurate prediction of pore pressure (PP) is crucial for optimizing drilling and reservoir management operations. This study investigates PP prediction in the West Baram Delta, Offshore Sarawak, a mature oil and gas field. The field exhibits varying depths of top overpressure zones, emphasizing the need for reliable PP predictions before the commencement of drilling operations. Sonic transit time, gamma-ray, and density logs are used to establish shale compaction trends and predict PP using Eaton’s and Miller’s methods. The predicted PP at the wellbore location was validated using a repeat formation tester data from two wells. Results showed that the Eaton DT method underestimates PP while the Miller method provides more accurate predictions at deeper overpressure zones. In shallow overpressure zones, where disequilibrium compaction is the dominant mechanism, the Eaton DT and Miller methods yield acceptable PP predictions. A 3D PP model demonstrated a better agreement with Eaton DT predictions at wellbore locations. The onset of overpressure was identified at approximately 2150 m depth. This study's out come provides valuable insights for PP evaluation in the West Baram Delta and in other similar geological settings prior to the drilling operation.

Determining Petrophysical Properties Using PLTs and Conventional Logs of the Middle Shale Member (4th Pay) of the Zubair Formation in the South Rumaila Oil Field, Southern Iraq

The petrophysical evaluation of the Zubair Formation's Middle Shale member (4th Pay) in the South Rumaila oil field has been conducted to characterize the reservoir and quantify its hydrocarbon prospects. Although the 4th Pay has a substantial oil reserve, it has never been studied. A gamma ray, resistivity, density, and neutron log were used to estimate the shale volume, porosity, water saturation, and hydrocarbon saturation for four oil-bearing subunits in the middle shale member (4th Pay), producing net pays of Middle Shale Member 7 (13 m), 10 (31 m), 20 (11 m) and 25 (16 m), respectively. Shale volume calculated using the Gamma-Ray Log method, averages between 0.05 % and 0.53 %. Effective porosity ranges from (0.08) % to (0.21) %, indicating good reservoir quality; permeability ranges from 0.9 to 193.6 mD, indicating well-sorted sands. Hydrocarbon saturation varies from 0.29 to 0.89, while water saturation ranges from (0.11) to (0.72). Production Log Tool and Nuclear Magnetic Resonance logs were used to estimate permeability. Production Logging Tool log permeability values ranged between 5.2 (Middle Shale Member 10, 3425 mD) and 49.7 (Middle Shale Member 25, 3448.5 mD), while Nuclear Magnetic Resonance log permeability values ranged between 5.81 (Middle Shale Member 10, 3425 mD and 49 (Middle Shale Member 25, 3448.5 mD). According to the correspondence between the two logs, the correspondence value equals 85%. Thus, the reservoir permeability can be calculated using the radial flow equation calculated from the Production Logging Tool log and generalized to areas where there is no core. Results show that permeability (K) is highest in subunits Middle Shale Member 20 and 25, which also have high porosity, low shale content, and fine grain sorting, making them key production subunits. Subunit Middle Shale Member, although lower in permeability, is the main reservoir with the most oil reserves. Subunits Middle Shale Member 5 and 7 have lower permeability due to high shale content and heterogeneity, with low porosity. Subunit Middle Shale Member 0 has very low permeability. The high oil shale content, due to its poor porosity and heterogeneity, renders it unproductive.

The post-depositional evolution of mesoproterozoic to early neoproterozoic tight sandstone reservoirs, São Francisco basin, Brazil

This study presents the first comprehensive assessment of unconventional gas reservoirs in the Proterozoic sequences of the intracratonic São Francisco basin, providing depositional and post-depositional characterization and insights into reservoir quality. Detailed stratigraphic, petrological, and seismic analyses, integrated with 2D seismic data, were performed to evaluate the influence of diagenesis and hydrothermal fluids. The drill core was drilled in the central sector of the basin and within the southern border of the Pirapora aulacogen, in a 303 m-thick interval of the Paranoá-Upper Espinhaço sequence. The petrological analysis shows arkoses and sublitharenites as well as conglomerates, siltstones and mudstones successions deposited in deltaic, fluvial braided and marine depositional systems. Six sedimentary environments were identified, from the base to the top: (i) deltaic plain, (ii) delta front, (iii) braided fluvial, (iv) delta front, (v) prodelta and (vi) basin plain. The integration of seismic data review three depositional domains, characterized by lower frequency arranged to transgressive-regressive cycles identified by gamma ray, resistivity and density logs. The studied strata review eo - and mesodiagenetic alterations, being also influenced by intrusion-related hydrothermal and contact metamorphism. The main diagenetic events resulted in cementation of calcite (0.84%), chlorite (1.23%) and silica (6.19%), which largely obliterated porosity at the shallow burial depths. The main secondary porosity is intergranular, with minor contributions to intragranular, fracture, moldic and vugular. Diagenetic processes as well as the influence of hydrothermal processes highlight the complexity of post-depositional processes in these units. The presence of natural helium and hydrogen within the basin also suggests that these reservoirs should be considered in future exploration campaigns targeting unconventional energy resources.

Net primary productivity of paleo-peatlands linked to deep-time glacial periods in the late Carboniferous and early Permian icehouse interval

Peatlands, an important organic carbon reservoir, play a crucial role in the global carbon cycle. The carbon accumulation of peatlands, reflected by net primary productivity (NPP), can have an impact on global carbon cycling and climate change. The late Carboniferous - early Permian is an icehouse period, during which numerous thick coal beds were accumulated in the North China Block (NCB) located within a low-latitude area, providing an opportunity for studying the carbon cycling under the glacial and interglacial climates. In this study, spectral analysis was performed on the natural gamma-ray (GR) logs of the Benxi, Taiyuan, and Shanxi formations of the late Carboniferous to early Permian in a borehole section located within the Ordos Basin in western NCB. Cyclic signals related to astronomical orbital parameters were identified, including long eccentricity (∼405 kyr), short eccentricity (∼125 kyr and ∼ 95 kyr), and obliquity (∼35.5 kyr). A floating astronomical time scale was established by using the long eccentricity signal, and this time scale was further used to constrain the durations of the accumulation of coal-forming paleo-peatlands. The paleo-peatland for the C8+9 coal seam (9 m thick) of the Taiyuan Formation lasted approximately 203 kyr, and the paleo-peatland for the C5 coal seam (4 m thick) of the Shanxi Formation lasted approximately 46 kyr. Using this timeframe and an estimation of carbon loss during coalification, the carbon accumulation rates of the late Carboniferous - early Permian low-latitude peatlands are calculated to be 104.7 ± 14.9 g·C·m−2·a−1for the C8+9 coal seam and 192.6 ± 11.6 g·C·m−2·a−1for the C5 coal seam. The NPP of the paleo-peatlands, which deducts a part of the carbon loss caused by the loss of CO2 and CH4, can be calculated from the carbon accumulation rates. The calculated average NPP of the paleo-peatlands for the C8+9 seam was 199 ± 28 g·C·m−2·a−1, and that of the C5 seam was 366 ± 22 g·C·m−2·a−1. In combination with the absolute time scale calibrated by high-precision UPb dates from Palougou section in western NCB, the depositional time of the investigated strata was constrained to be from 300.1 ± 0.5 Ma to 294.3 ± 0.5 Ma. The coal seams of the late Carboniferous to early Permian in the NCB correspond to an interglacial interval around ∼298 Ma. The peatland with a lower NPP corresponds to the warming stage and the peatland with a higher NPP corresponds to the cooling stage. This implies that a lower NPP of paleo-peatland tends to be less efficient in carbon storage, and could not reduce the atmospheric CO2 substantially. In contrast, a higher NPP of paleo-peatland tends to accelerate carbon fixation, leading to temperature decrease and the termination of interglacial interval in early Permian. The results of this study could provide insights into the relationship between the development of paleo-peatlands and the record of the paleoclimates during the same period, and could be helpful for the prediction of future climate change.

Analysis of lake level fluctuations in the Early Cretaceous Songliao Basin supports aquifer eustacy

Clear geochronology and precise interpretation of sequence stratigraphy enhance our understanding of continental lake-level evolution. The Songliao Basin features well-preserved Cretaceous continental strata. Nevertheless, the correlation between lake-level fluctuations and global sea-level changes remains ambiguous. High-resolution gamma ray logging data were used to analyze the cyclical stratigraphy of the lower member of the Early Cretaceous Shahezi Formation in the Songliao Basin. X-ray fluorescence experiments characterized the sedimentary environment of this formation. Lake-level variations in the lower part of the Early Cretaceous Shahezi Formation were reconstructed using sedimentary noise modeling based on finely tuned gamma ray logging data. Time series analysis using the tuned gamma ray data established an astronomical timescale of approximately 2.4 Myr within the lower section of the early Cretaceous Shahezi Formation in the Songliao Basin. A volcanic ash layer dating (118.20 ± 1.5) Ma from the base of the Shahezi Formation served as an anchor point, providing an absolute astronomical timescale ranging from 115.80 to 118.20 Ma for the study region. The sedimentary model indicates that variations in paleowater depth within the lower section of the Shahezi Formation closely match fluctuations observed in the Fe/Mn index, which reflects paleowater depth changes. This introduces a novel approach to assess changes in continental lake levels. The sedimentary noise model revealed a notable obliquity cycle of about 1.2 million years, strongly associated with fluctuations in lake levels. This indicates that prolonged obliquity periods affect lake level variations. Intriguingly, when lake levels rise, global sea levels concurrently decline, highlighting an inverse relationship between these phenomena. This observation offers insights into how long-term obliquity-driven climate changes regulate sea and lake levels.

Using the Conventional Method to Estimate Water Saturation of Khasib Formation in East Baghdad Oil Field Central Area

The water saturation level may change due to geological changes or human intervention, like earthquakes, excavation, injection, or extraction of fluids from underground reservoirs. This work helps the reader understand how to use electrical logging to acquire a suitable water saturation value in resistivity and dielectric studies. In the East Baghdad field, two wells that were drilled into the Khasib Formation had their petrophysical parameters assessed using IP software. This allowed for the determination of water saturation using the available well logs, including neutron, density, SP, sonic, gamma ray, and resistivity logs for wells EB-25, and EB-29. The limestone section in the Khasib formation represents calcite, the main mineral found. We counted the amount of shale in each well using gamma-ray logs. The porosity at the reservoir unit was determined using a sonic log. Archie's equation and resistivity data are also used to compute fluid saturation. Taking m=n=2, a=1, x=1.76, and Rw=0.033 for Khasib formation. The link between Archie's and Raiga's coefficients, a, m, n, and x, is primarily the basis for this discussion. There is high agreement when this equation is used. In clean formations (non-shaly). this formula can be regarded as a reliable indicator of water saturation in situations where porosity techniques other than acoustic logs are unavailable. It's important to note that this equation provides accurate and realistic answers for rocks with transit times between 40 and 150 As/ft. Porosity knowledge is not required for such an assessment.

Using well log data to predict rock compressibility and elasticity in Zubair formation/ southern of Iraq

The mechanical characteristics of rocks such as elasticity (Young's modulus, Poisson's ratio, and unconfined compressive strength) play an essential in sand production analysis, well design, and borehole stability assessment. In this study, the mechanical characteristics of rocks were indirectly estimated using gamma ray, density, and acoustic (compressional and shear) log data from well RU-X in the Rumaila oil field, specifically for the Zubair Formation. These estimated properties were compared to direct measurements obtained from triaxial and uniaxial mechanical tests conducted on well RU-X. The results showed a significant similarity between the indirect estimates and the direct measurements, indicating their reliability for sand production analysis and their valuable contribution to constructing the geomechanical model. Moreover, the static profile of Poisson's ratio was validated using laboratory core test results, demonstrating reasonable agreement. The validation process involved comparing laboratory-derived measurements with actual field measurements in the Zubair Formation. The higher Poisson's ratio observed in the shale was attributed to the slower propagation of acoustic waves, resulting in a good matching with an R2 value of 0.77. On the other hand, the lower Young's modulus in the shaly formations indicated lower resistance to deformation, with a comparative ratio of R²=0.96. Static measurements, which consider various influencing factors, provide a more realistic representation of rock behavior under different conditions. Regarding unconfined compressive strength, the comparative ratio was R²=0.83.

Pattern recognition approach (PRA) for identifying oil reservoir lithology of Camaal oil field, Yemen

The accurate determination of reservoir lithology remains a challenge in petroleum engineering. There are some conventional techniques available to determine the lithology. However, the application of those techniques has been long and complex. So, the main goal of this study is to simplify the identification of reservoir lithology. This paper presents a Pattern Recognition Approach (PRA) to identify the reservoir lithology simply and accurately. It is type of artificial neural network. Four wells from the Camaal Field were chosen to develop this approach. Around 32400 data points from the previous wells were digitized. The PRA approach used depth, gamma ray, lithology, sonic, neutron, and density logs as inputs. The model classifies lithology into permeable and impermeable rocks, further categorizing them into clastic and carbonate rocks, and subsequently into specific types into sand, sandstone, dolomite and limestone. The results show that the proposed approach provides a suitable prediction of lithology with higher accuracy compared with actual lithology. The model demonstrates high accuracy rates in identifying various lithologies, with overall accuracies of 76.2% for permeable/impermeable rocks, 94.9 for clastic/carbonate rocks, 86.2% for sand/sandstone, and 92.8% for dolomite/limestone.

Petrophysical identification and characterization of Cenozoic hydrocarbon reservoir zones at the R field, northern Rovuma Basin, Tanzania

ABSTRACT Cenozoic reservoirs of northern Rovuma Basin, Tanzania, have not yet been characterized in detail. Combined well log patterns and fracture indicators have not been presented before. This work used petrophysical analysis of density, neutron, gamma ray (GR), resistivity and sonic well logs to characterize reservoir zones at the study area. Twelve reservoir zones (A–L) have been identified based on lithological properties and resistivity anomalies. The presence of hydrocarbons in these zones is concluded based on acoustic parameters involving acoustic impedance and V p / V s ratio, and ∆TC and neutron porosity crossplots. Fractured sedimentary fills in different stratigraphic levels were assessed based on resistivity-logs crossplots. Results show that eleven zones (A–F and H–L) are gas-filled and zone G is devoid of hydrocarbon. We have shown that, in areas with resistivity anomaly, all GR-resistivity combined patterns may be used to indicate hydrocarbon bearing layers except when there is a co-occurrence of funnel-shaped pattern for both logs. Distribution of fractures and hydrocarbons suggest the existence of compartmentalized reservoirs. These reservoirs have been discussed based on a geoseismic model created to support our arguments. Our technique has allowed easier identification of hydrocarbon bearing zones through well Rx. This technique may be applied in other sedimentary basins.

Algorithm for constructing unmeasured logging curves (pseudo-logging) in wells of developed fields

In many cases, the diagrams of some logs in an old well of producing fields are not recorded for some reason or another. Currently, the importance of reassessing the efficiency of such fields is obvious, while the absence of some log curves causes great difficulties. In particular very few studies with acoustic logging (AL) have been carried out in old wells. On the other hand, in 1960-80, among the studies carried out in wells, preference was given to various types of electrical logging (EL), neutron-gamma logging (NGL), and gamma ray logging (GRL). To overcome such difficulties, it is necessary to create diagrams of unmeasured well logs or pseudo-logs. Nowadays, there are methods for constructing pseudolog curves. For example, the method of statistical analysis is widely used. However, in most cases, their implementation is very complex and requires a lot of work. We have developed a new method to construct pseudo-log curves. At first, the propagation velocity of elastic waves in rocks is studied based on seismic survey (SS) and AL data, which are performed presently on the investigation field. Based on this information, objects of interest to the oil industry are selected, their geometric dimensions are determined, the contour of oil and gas saturation is assessed, areas with abnormally high reservoir pressure are studied, the process of accumulation deposition is re-examined, and the preparation of an optimal well network diagram for the effective exploitation of the field is determined. Further, based on the long-ago measured curves of electrical loggings and neutron-gamma logging, we obtain the pseudo-log AL curve in old boreholes and the data SS. The proposed method was tested in 48 wells in the Bahar field (Azerbaijan). The method has shown high efficiency, its reliability is confirmed by independent data. The results of the method help in solving several geological problems: monitoring changes in petrophysical quantities of rocks between wells during development; study of well formations located below the design depth; recalculation of hydrocarbon reserves; determination of the location for drilling new wells, etc

New capabilities of application of gamma-ray logging in borehole geophysical investigations through steel string

Borehole geophysical investigations through steel string (both drill and casing) are becoming increasingly important. Gamma-ray logging (GR) is a mandatory and one of the main methods for investigating oil-and-gas reservoirs and near-surface rocks. It is characterized by high efficiency under the aforementioned measurement conditions. GR is used to identify bed boundaries, evaluate lithology, estimate shaliness, identify reservoirs, etc. Traditionally, the determination of granu- lometric shaliness is the main goal of quantitative interpretation for GR. Gamma-ray logging has been successfully used for many decades. However, increasing the accuracy of determining granulometric shaliness, expanding the informativeness of the method to determine mineral clayness, and developing procedures for using GR data in estimating other petrophysical parameters are topical issues. Based on logging materials, the paper examines the features of using the main interpretation parameter of GR (the relative difference parameter – index ΔІγ) in boreholes in the presence of a steel column and proposes new methodological approaches to the use of GR data when interpreting measurements in oil-and-gas and engineering-geological boreholes. Through numerous ex- amples, the invariance (constancy) of the interpretation parameter of gamma-ray logging has been proven under different borehole conditions, types of logging tools, etc. An approach has been proposed for obtaining optimal reference values when determining this parameter, which increases the accuracy of estimating shaliness parameters. A method has been developed for determining mineral clayness, which is an important quantity in itself and is necessary for determining other petrophysical parameters from loggingdata. A multiplicative method has been created for determining the total porosity of shaly rocks using a combination of neutron logging and gamma-ray logging, which makes it possible to avoid using the hydrogen index of clay minerals. The novelty of the developments is supported by patents, and their effectiveness is confirmed by the results of borehole tests and comparisons with independent determinations of parameters (laboratory core studies, traditional methods of interpretation). The proposed approaches are an important component of the investigation technologies for oil-and-gas reservoirs and near-surface rocks, developed at the Institute of Geophysics of the National Academy of Sciences of Ukraine.

Machine learning based reservoir characterization and numerical modeling from integrated well log and core data

In recent years, artificial intelligence applications have proved useful for analyzing petrophysical data and characterizing subsurface formations. Conventional reservoir characterization employs core data measurements and local correlations between porosity and permeability as input data for reservoir property modeling. The vertically and laterally sparse nature of core and well-log data respectively present challenges in developing three-dimensional models of subsurface properties. In addition, a strong correlation between porosity and permeability as well as reliable core measurements are not always available. The complex interrelationship within the dataset is further evidenced through the application of parametric and nonparametric approaches, which are commonly utilized in conventional regression methods. The parametric method yielded an RMSE value of 0.335 and the nonparametric, 0.418, necessitating the implementation of an algorithm capable of modeling the complex relationship within the dataset. The proposed approach uses an improved machine learning (ML) workflow to generate and evaluate the performance of different porosity and permeability models from integrated well-log and core data, comparing the performance to traditional methods. The workflow combines a suite of unsupervised and supervised ML methods in establishing the relationship between porosity and permeability for different zones. Unsupervised ML algorithms such as K-means, K-median, and hierarchical clustering are applied to the data to generate groups with similar trends. Supervised ML regression methods such as Gaussian process regression (GPR), neural network regression, support vector machine (SVM) regression, and ensemble regression are implemented to predict permeability based on gamma ray, bulk density, and deep resistivity logs. This method demonstrated enhanced dataset modeling, as indicated by the decrease in RMSE values when using the GPR following the hierarchical clustering technique. This recorded RMSE values as low as 0.125, which is roughly 34% lower than those observed with traditional methods. Two of the predicted models were assessed using a history-matching process, which showed an almost perfect fit for nearly all the wells, thereby confirming the accuracy of the regression.

Modelowe i empiryczne podejście do oceny parametrów petrofizycznych skał na przykładzie złoża gazu Dhodak w Pakistanie

The main aim of the presented study was to perform an analysis of petrophysical rock parameters for the Pab Formation, which is a potential reservoir, located at four-depth intervals within the Dhodak Gas Field. The Pab Formation in the studied wells is mostly composed of the well-sorted sandstone. The intervals for the study were selected by applying a gamma ray log cut-off of 40%. A Matlab code that is user-friendly and contains empirical and numerical models (Raymor’s, Han's, Castagna’s, Jizba, Blangy, and Gardner's models) was designed with the purpose of providing an interpretation of rock physics parameters in reservoirs. The results indicate that the Pab Formation is mainly saturated with hydrocarbons and possesses sufficient porosity (i.e. between 8 and 38%). According to the cross-plot models, there are a few zones within the Pab Formation that are weakly consolidated. These zones are located at depths of 2377–2404 m and 2418–2430 m. However, the same area is classified as tight gas sandstone and is located between 2330 to 2343 m. The results of the study indicate that the Pab Formation is potentially saturated with hydrocarbons. These findings were based on structural and rock physics assessments of the study area. The approach presented in this work is also helpful for structural trap evaluation. In addition, the petrophysical rock parameter models developed can contribute to reducing the risks associated with hydrocarbon exploration, enabling accurate representation of subsurface conditions and improving reservoir forecasting capabilities in oil producing areas.

Microfacies Analysis and Evolution of Porosity of the Albian-Early Cenomanian Mauddud Formation from Selected Wells in Ratawi Oilfield, Southern Iraq

Mauddud Formation (Albian stage-the Early Cretaceous) is an important oil reservoir in Ratawi field of southern Iraq. Four wells, R T-2, R T-3, R T-6, and R T-7, located 70 km northwest of Basra, were selected to study microfacies properties and petrophysical associations with the probability of oil production. Seventy-seven core samples are collected, and thin sections for petrographic analysis. The self-potential, Gamma-ray, resistivity, and porosity logs are used to determine the top and bottom of the Mauddud Formation. Water saturation of the invaded and uninvaded zones, shale volume, and porosity were calculated. The study area results showed that the quantity of shale is less than 15% for most of the wells, and the dominant porosity is the secondary porosity. In contrast, the primary porosity is low in all study wells, and the formation contains varying proportions of producing hydrocarbons. The results suggest the formation comprises light-colored dolomitized lime and pseudo-oolitic white limestone with green to blue-gray shale. The petrographic analyses of 65 thin sections of the Mauddud Formation reveal that most skeletal grains are shallow marine-derived faunas, while non-skeletal grains include peloids and ooids. The facies analysis showed that the Mauddud Formation was deposited within different sedimentary environments ranging from deep to very shallow environments, which was represented by a large variation in Five microfacies and ten submicrofacies, the microfacies "Mudstone, wackestone, packstone, grainstone, and Rudstone "submicrofacies" Peloidal wackestone to packstone, Bioclastic wackestone to packstone, Miliolids wackestone to packstone,Orbitolina wackestone, Peloidal packstone to grainstone, Orbitolina packstone, Mudstone to wackestone,Bioclastic wackestone, Miliolids and Bioclastic wackestone to packstone, Bioclastic packstone, and Peloidai grainstone". This variance suggested that the formation might be deposited within the carbonate platform's variety of environment, such as "deep sea, restricted, shallow open marine, mid-ramp, rudist biostrome, and shoal." Seven different kinds of pores have been found in the carbonate sideman of the Formation: Interparticle, Intraparticle, moldic, Vuggy and fracture.

A Petrophysical Properties Evaluation Study of a Niger Delta Field Using Well Logs and Core Data

A petrophysical properties evaluation study of an onshore field “NEMA” was carried out to assess the petrophysical characteristics of the field in the eastern portion of the Niger Delta Basin. This was done in an effort to determine the hydrocarbon prospect and oil productivity potential of the reservoirs. The net/gross thickness was one of the examined parameters. (pay zone thickness), porosity, permeability, fluid saturation which were all inferred from geophysical wireline logs and core data. The wireline logs used in the investigation included density, resistivity, gamma ray, and spontaneous potential logs for five different wells from the “NEMA” oil field. Five reservoirs designated, NEMA 01, NEMA 04, NEMA 07, NEMA 09, and NEMA 12 were identified and correlated across the five wells in the field using gamma ray logs and shale resistivity markers. Based on the computation of petrophysical parameters, the porosity of the reservoirs ranged from 0.22 (22%) to 0.31 (31%); the permeability ranged from 851.58 mD to 1005.05 mD; the average net-to-gross thickness of the reservoir sand bodies was 140 ft; the water saturation value was less than 45%, while the hydrocarbon saturation was greater than 55%. We equally observed that our computed petrophysical parameters from well log data correlated well with the core computed values in NEMA 09. In conclusion, the delineated reservoirs were categorized as good hydrocarbon reservoirs. These results suggest huge hydrocarbon potential and a reservoir system whose performance is considered satisfactory for hydrocarbon 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.

Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.

Carbonate reservoir characterization and permeability modeling using Machine Learning ـــ a study from Ras Fanar field, Gulf of Suez, Egypt

Predicting facies and petrophysical properties along and between wells is challenging in carbonate reservoir modeling. In the Nullipore carbonate reservoir, Ras Fanar field, depositional and long-term diagenetic processes result in a high degree of heterogeneity and complex distribution of facies, which in turn affect the reservoir quality. This provides a significant obstacle to building accurate geological models. This study integrates thin sections, routine core analyses, and well logging data to overcome such difficulties and model the Nullipore carbonate facies and permeability. The detailed petrographic analysis revealed the existence of seven microfacies in the reservoir, which are summed up into three facies associations (FAs), each of which represents a specific reservoir rock type (RRT): (1) supratidal FA, (2) intertidal FA, and (3) shallow subtidal FA. The three FAs were correlated with the gamma-ray logs to create facies logs for the studied wells, which were further populated via the Truncated Gaussian Simulation method. Cross-validation was used to evaluate the model's accuracy. The analysis of the available core data infers that the three RRTs are prospective and have a wide permeability distribution. However, RRT3 constitutes the best reservoir quality. The sedimentological analysis revealed that the long-term diagenetic events, involving the dolomitization of limestone and the dissolution of allochems have a major role in improving the pore connectivity and permeability of the reservoir. Fracture characterization discloses that fractures play a significant role in fluid storage and migration. Three Machine Learning (ML) models, including Adaptive boosting (AdaBoost), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were developed to integrate the RRTs, porosity, and permeability to improve permeability prediction. Statistical analysis revealed that the XGB model outperforms other models and exhibits the highest prediction performance. The present study provides further insights into the characterization and modeling of facies and permeability of complex carbonate reservoirs. It can be applied in similar geological settings to better interpretation of depositional and diagenetic controls on reservoir quality assessment and aid in the field development plan.