Spectral Gamma Ray Logs Research Articles

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.

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.

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.

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.

Total organic carbon variability of lower Jurassic successions in the Mandawa Basin, SE Tanzania

Over many decades, an understanding of total organic carbon (TOC) variability in various sedimentary basins has been constrained by the availability of suitable samples for geochemical analysis. Many attempts rely on selected sidewall core or cuttings samples, which do not provide a continuous lithological record of the succession. As a result, TOC variability as a response to facies evolution is often underestimated. An advancement in TOC evaluation from well logging techniques often has permitted a continuous analysis of source rock intervals, while eliminating issues associated with obtaining discrete physical samples. Geochemical and wireline geophysical logs investigation here aimed at constraining TOC variability and depositional conditions prevailing during the early Jurassic in the Tanzanian Mandawa Basin (Mbuo, Nondwa and Mihambia Formations), which to date are poorly documented. TOC was deduced from both well cuttings samples and wire log data. Depositional conditions were defined via the use of spectral Gamma-Ray (Th/U, U) logging and lithofacies data. Sediment composition and TOC from cuttings were compared to illustrate how organic matter distribution is influenced by changes in sediment type. Complex mineral composition of heterolithic facies dominated log-response, complicated log interpretation, and thus limited the effectiveness of well logging techniques to predict TOC. Cuttings data reveal three organic-rich intervals, i) at the transition of Upper Mbuo to basal Nondwa Formation with the highest TOC of 3.5 (wt%), ii) in the Nondwa Formation with a TOC of 2 (wt%), and iii) in the Mihambia Formation with a TOC of 2.8 (wt%). An inverse linear relationship between Th/U and TOC signifies relatively shallow deposition in a well-mixed water column under oxic conditions. Intervals with higher TOC imply organic matter preservation under suboxic conditions upon two marine incursions in the Mihambia and Nondwa Formations, whereas a limnic environment prevailed at the Upper Mbuo to Nondwa transition. Inverse trends in TOC versus clastic components, a positive linear relationship between TOC and carbonates, and higher amount of clastics (>57 wt%) in the Mbuo and Mihambia Formations suggest a dominantly clastic sedimentary system with relatively stable carbonate and organic matter input in proximity to the source area. We, therefore, argue that the organic rich intervals observed in the Upper Mbuo, Nondwa and Mihambia Formations were deposited during periods of higher organic productivity driven by a rise in eustatic sea level, and coincide with a decline in terrigenous input from the hinterland.

An astronomical age-depth model and reconstruction of moisture availability in the sediments of Lake Chalco, central Mexico, using borehole logging data

Understanding the moisture history of low latitudes from the most recent glacial period of the latest Pleistocene to post-glacial warmth in continental tropical regions is hampered by the lack of continuous time series. We conducted downhole spectral gamma (γ) ray and magnetic susceptibility logs over 300 m of lacustrine deposits of Lake Chalco (Mexico City) to reconstruct an age-depth model using an astronomical and correlative approach, and to reconstruct long-term moisture availability. Our results suggest that the Lake Chalco sediments contain several rhythmic alternations with a quasi-cyclic pattern comparable to the Pleistocene benthic stack. This allows us to calculate a time span of about 500,000 years for this sediment deposition. We developed proxies for moisture, detrital input, and salinity, all based on the physical properties of γ-ray spectroscopy and magnetic susceptibility. Our results indicate that Lake Chalco formed during Marine Isotope Stage 13 (MIS13) and the lake level gradually increased over time until the interglacial MIS9. Moisture content is generally higher during interglacials than during glacials. However, two periods, namely MIS6 and MIS4, have higher moisture contents. We developed a model by comparing the obtained moisture proxy with climatic drivers, to understand how different climate systems drove effective moisture availability in the Chalco sub-basin over the past 500,000 years. Carbon dioxide, eccentricity, and precession are all key drivers of the moisture content of Lake Chalco over the past 500,000 years.

Lithofacies of early Jurassic successions derived from spectral gamma ray logging in the Mandawa Basin, SE Tanzania

Spectral gamma ray log (SGRL) data has been widely employed to derive subsurface lithological information and to perform well correlation when the core is not available. Previous studies mainly targeted homogenous facies whereas few attempts addressed heterolithic successions. The SGRL investigation here aimed at deriving lithofacies and further constrain conditions of deposition prevailing during the early Jurassic (Mbuo, Nondwa, and Mihambia Fms.) in the Mandawa Basin, onshore Tanzania, where core and outcrop data is rare. By use of binary discrimination diagrams of U, Th, and K, composition fields were defined for various lithofacies types (black shale, red shale, siltstone, sandstone, chert, anhydrite, halite, and carbonate) compiled from literature data. Sediment composition for two wells in the Mandawa Basin was then deduced from cuttings and SGRL data and compared with literature-derived geochemical provenance data from adjacent regions and strata. Binary discrimination diagrams revealed the presence of multi-mineral components in the heterolithic facies type, which in concert with diagenetic and detrital mineral dilution effects, led to low compositional contrasts between provenance fields and to overlap of different lithofacies types, complicating interpretation and limiting the effectiveness of the SGRL-based facies differentiation. For the early Jurassic strata, an upward increase in the Th/K ratio suggests an enhanced terrigenous influx into the basin due to intensified weathering. High Th/U and Th/K in the study region indicate a relatively shallow, well mixed, and oxic depositional setting in proximity to the source area opposing the more distal, deeper, and reduced depositional settings that prevailed in the southern central part of the basin. Th/U and Th/K excursions in the Nondwa and Mihambia formations suggest a shift towards less oxic depositional conditions upon two major marine incursions, corroborated by a decline in the clastic fraction and concomitant increase in carbonate.

Interval inversion based well log analysis assisted by petrophysical laboratory measurements for evaluating tight gas formations in Derecske through, Pannonian basin, east Hungary

The evaluation of Hungarian tight gas formations requires the use of innovative interpretation technologies because of diverse geological environment, heterogeneous dataset including several variables measured with different accuracies, and reservoirs under varying petrophysical conditions. Conventional well logging methods based on the use of limited number of wireline logs make only a quick-look interpretation, in which a limited number of unknowns is determined, and there is a non-negligible risk for occurrence of ambiguity. In order to increase the overdetermination (data-to-unknowns) ratio and the estimation accuracy and reliability, all suitable well logs are to be jointly processed in a more effective manner. To estimate the total organic carbon content together with porosity, water saturation, shale content and rock matrix volumes with higher accuracy, a new inversion methodology is introduced that is tested in tight gas reservoirs. A differential evolution algorithm-based interval inversion method is developed that is combined by nano-permeability and high precision porosity laboratory measurements for the detection and evaluation of Hungarian tight gas reservoirs. Spectral gamma-ray, neutron-porosity and electric resistivity logs are jointly analyzed using the interval inversion approach. By solving a highly overdetermined inverse problem, the effective porosity, water- and gas saturation, shale and sand content, as well as the kerogen volume are estimated in a reliable inversion procedure. The silt and clay content of Miocene tight gas formations are separated and both determined by inversion, while the total organic carbon content is derived from the inversion results. The permeability log is directly derived from the inverted porosity values using the local porosity versus permeability relation established by multi-well core analysis. The combination of in situ wireline log data analysis and porosity and nano-permeability laboratory measurements is shown to be advantageous for a more reliable interpretation of unconventional hydrocarbon formations. An added advantage of the interval inversion method is the calculation of depth dependent errors of the estimated petrophysical quantities. It is shown that total organic carbon and other volumetric parameters can be determined with high accuracy in one interpretation procedure. In the Derecske basin, East Hungary, gas indications are confirmed by interval inversion, that were previously made it probable according to measurements made on poor core samples and rather noise-sensitive local well logging inversion procedure. The joint evaluation of new core data and original wireline logs using the proposed methodology may reveal further potential tight gas resources in the studied area. The Hungarian case study confirms that the suggested workflow may serve a new alternative for an improved evaluation of tight gas formations in different other hydrocarbon fields.

High-resolution late Paleozoic cyclostratigraphy and tectonic evolution of the Keeler Basin, California, southwest Laurentia

Abstract Cyclic strata exposed in the Inyo Mountains of eastern California contain a continuous 6 m.y. record of deep marine deposition that spans the Pennsylvanian–Permian boundary. To better understand the geologic evolution of southwest Laurentia and the role of glacially driven eustasy in upper Paleozoic stratigraphy, we measured two detailed ∼600 m composite stratigraphic sections of the Keeler Canyon Formation and collected a handheld spectral gamma ray log. Post-depositional deformation complicates field relationships, but 1:5000 scale mapping of faults and folds permits assembly of two continuous sections. Measured strata alternate at the 5–20 m scale between intervals of fine-grained laminated marlstone and intervals of mixed carbonate and siliciclastic turbidites and debrites. Based on facies characteristics and a prominent marker horizon, we reassign the Pennsylvanian-Permian age upper Salt Tram unit of the upper Keeler Canyon Formation to a new Estelle Member. We estimate sediment accumulation rates within the Keeler Canyon Formation using published conodont biostratigraphy and correlative U-Pb geochronology from Eastern Europe combined with spectral analysis and timescale optimization using the Astrochron R package. Evolutive harmonic analysis of gamma ray-derived element concentrations reveals prominent bundled periodicities that are consistent with both long and short eccentricity cycles. Average sediment accumulation rates calculated using the time scale optimization function of Astrochron suggest a gradual increase from 40–60 m/m.y. to ∼120 m/m.y. during the late Pennsylvanian and then a minima of ∼50 m/m.y. near the Pennsylvanian–Permian boundary, which is followed by an increase to ∼175 m/m.y. into the Early Permian. This trend in sediment accumulation rates and subsequent Permian contractile deformation are compatible with flexural subsidence in a SW-migrating foreland basin related to the southern part of the late Antler orogenic system.

Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach

Abstract Total organic carbon (TOC) is an essential parameter that indicates the quality of unconventional reservoirs. In this study, four machine learning (ML) algorithms of the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), functional neural networks (FNN), and random forests (RFs) were optimized to evaluate the TOC. The novelty of this work is that the optimized models predict the TOC from the bulk gamma-ray (GR) and spectral GR logs of uranium, thorium, and potassium only. The ML algorithms were trained on 749 datasets from Well-1, tested on 226 datasets from Well-2, and validated on 73 data points from Well-3. The predictability of the optimized algorithms was also compared with the available equations. The results of this study indicated that the optimized ANFIS, SVR, and RF models overperformed the available empirical equations in predicting the TOC. For validation data of Well-3, the optimized ANFIS, SVR, and RF algorithms predicted the TOC with AAPEs of 10.6%, 12.0%, and 8.9%, respectively, compared with the AAPE of 21.1% when the FNN model was used. While for the same data, the TOC was assessed with AAPEs of 48.6%, 24.6%, 20.2%, and 17.8% when Schmoker model, ΔlogR method, Zhao et al. correlation, and Mahmoud et al. correlation was used, respectively. The optimized models could be applied to estimate the TOC during the drilling process if the drillstring is provided with GR and spectral GR logging tools.

A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations. Doi: 10.28991/HEF-2021-02-02-01 Full Text: PDF

Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale From the Spectral Gamma-Ray and Based on the Artificial Neural Networks

Abstract The evaluation of the quality of unconventional hydrocarbon resources becomes a critical stage toward characterizing these resources, and this evaluation requires the evaluation of the total organic carbon (TOC). Generally, TOC is determined from laboratory experiments; however, it is hard to obtain a continuous profile for the TOC along the drilled formations using these experiments. Another way to evaluate the TOC is through the use of empirical correlation, and the currently available correlations lack the accuracy especially when used in formations other than the ones used to develop these correlations. This study introduces an empirical equation for the evaluation of the TOC in Devonian Duvernay shale from only gamma-ray and spectral gamma-ray logs of uranium, thorium, and potassium as well as a newly developed term that accounts for the TOC from the linear regression analysis. This new correlation was developed based on the artificial neural networks (ANNs) algorithm which was learned on 750 datasets from Well-A. The developed correlation was tested and validated on 226 and 73 datasets from Well-B and Well-C, respectively. The results of this study indicated that for the training data, the TOC was predicted by the ANN with an AAPE of only 8.5%. Using the developed equation, the TOC was predicted with an AAPE of only 11.5% for the testing data. For the validation data, the developed equation overperformed the previous models in estimating the TOC with an AAPE of only 11.9%.

Geothermal Resources Recognition and Characterization on the Basis of Well Logging and Petrophysical Laboratory Data, Polish Case Studies

Examples from the Polish clastic and carbonate reservoirs from the Central Polish Anticlinorium, Carpathians and Carpathian Foredeep are presented to illustrate possibilities of using well logging to geothermal resources recognition and characterization. Firstly, there was presented a short description of selected well logs and methodology of determination of petrophysical parameters useful in geothermal investigations: porosity, permeability, fracturing, mineral composition, elasticity of orogeny and mineralization of formation water from well logs. Special attention was allotted to spectral gamma-ray and temperature logs to show their usefulness to radiogenic heat calculation and heat flux modelling. Electric imaging and advanced acoustic logs provided with continuous information on natural and induced fracturing of formation and improved lithology recognition. Wireline and production logging were discussed to present the wealth of methods that could be used. A separate matter was thermal conductivity provided from the laboratory experiments or calculated from the results of the comprehensive interpretation of well logs, i.e., volume or mass of minerals composing the rocks. It was proven that, in geothermal investigations and hydrocarbon prospection, the same petrophysical parameters are considered, and well-logging acquisition equipment and advanced methods of processing and interpretation, developed and improved for almost one hundred years, can be successfully used in the detection and characterization of the potential geothermal reservoirs. It was shown that the newest (current investment)—as well as the old type (archive)—logs provide useful information.

Astronomical forcing variations of the Upper Dalan Member (Late Permian) in the South Pars gas field, Persian Gulf, Iran

• Orbital cycles detected the in the Late Permian strata of South Pars gas field Iran. • Prolific gas reservoirs units K1 to K4 units) formed over a time interval of 4.8 ± 0.3 Myr. • Sediment accumulation rates of the Upper Dalan Member range from ~3.5 to ~18 cm/kyr. • Depositional duration of the Upper Dalan Member estimated to be 3.2 Myr ± 0.2 Myr. • Eight 405-kyr long-eccentricity cycles were identified from well log analysis. The Late Permian sedimentary succession in the Iranian offshore Persian Gulf, formed under arid paleoclimate conditions, has been poorly studied in terms of astronomical forcing evidence. In this study, petrophysical logs are applied as paleoclimate proxies. They are used to investigate the astronomical forcing of the arid paleoclimate during the Late Permian together with the sediment accumulation rates (SAR) of the Upper Dalan Member (UDM) in two wells drilled in the South Pars gas field. Evidence of Milankovitch cycles (eccentricity, obliquity, and precession) was detected in both of the studied wells. By application of a combination of different methods (e.g., spectral analysis, COCO/eCOCO analysis), the SAR of the UDM was estimated to be in the range of ~3.5 to ~18 cm/kyr. Based on the 405 kyr long-eccentricity cycles extracted from the spectral gamma ray (SGR) logs, a floating astronomical time scale (ATS) of 3.2 ± 0.2 Myr was established for the UDM. This is equivalent to the depositional duration of the UDM in the studied wells. The Kangan Formation and UDM (K1 to K4 units) of southwestern and offshore of Iran were deposited during an astronomical duration of 4.8 ± 0.3 Myr. Additionally, the accumulation of organic matter in the studied reservoir units was compared with the SAR. The effects of SAR on the total organic carbon (TOC) variations were detected and correlated in four different segments of the UDM.