Gamma-ray Logs Research Articles

Palynofacies Analyses of HA-001 well, Offshore Depobelt, Niger Delta Basin, Nigeria

Palynofacies studies of the strata penetrated by HA-001 well were carried out with the aim to establish palynostratigraphic zones and paleodepositional environments. The standard acid palynological method of sample administration was employed to recover the palynofacies, soaking and washing of samples with water for sedimentological analyses and integration of Gamma Ray Log, palynofacies and sedimentological data for interpretation of paleodepositional environments. The study established two new assemblage biozones namely Cyperaceaepollis sp. – Nympheaepollis clarus, and Stereisporites sp. within the sedimentary intervals penetrated in the wells. The Cyperaceaepollis sp. – Nympheaepollis clarus biozones were further subdivided into Nympheaepollis clarus – Echitriletes pliocenicus and Cyperaceaepollis sp. – Elaeis guineensis subzones. The Late Miocene age is associated with the biozones. The lithologic, textural and wireline log data indicated that the intervals studied in the HA-001 well belong to the Benin Formation and Agbada Formation. Index minerals and accessories are dominated by ferruginous materials, glauconite pellets, carbonaceous detritus, shell fragments and pyrites plus minor mica flakes. Lower delta plain, delta front and prodelta environment of deposition were inferred for the studied well intervals. The research has conformed to international stratigraphic guide for establishment of biozones which serves to increase the knowledge of high resolution biostratigraphy.

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.

Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence

Machine learning (ML) to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields. Meandering, braided fluviatile depositional environments tend to form clastic sequences with laterally discontinuous layers due to the continuous shifting of relatively narrow sandstone channels. Three cored wellbores drilled through such a reservoir in a large oil field, with just four recorded well logs available, are used to classify four lithofacies using ML models. To augment the well-log data, six derivative and volatility attributes were calculated from the recorded gamma ray and density logs, providing sixteen log features for the ML models to select from. A novel, multiple-optimizer feature selection technique was developed to identify high-performing feature combinations with which seven ML models were used to predict lithofacies assisted by multi-k-fold cross validation. Feature combinations with just seven to nine selected log features achieved overall ML lithofacies accuracy of 0.87 for two wells used for training and validation. When the trained ML models were applied to a third well for testing, lithofacies ML prediction accuracy declined to 0.65 for the best performing extreme gradient boosting model with seven features. However, an accuracy of ∼0.76 was achieved by that model in predicting the presence of the pay bearing sandstone and siltstone lithofacies in the test well. A model using only the four recorded well logs was only able to predict the pay-bearing lithofacies with ∼0.6 accuracy. Annotated confusion matrices and feature importance analysis provide additional insight to ML model performance and identify the log attributes that are most influential in enhancing lithofacies prediction.

Deep Learning for Multiwell Automatic Log Correction

Researchers have dedicated numerous applications of machine-learning (ML) techniques for fi eld-scale automated interpretation of well-log data. A critical prerequisite for automatic log processing is to ensure that the log characteristics are reasonably consistent across multiple wells. Manually correcting logs for consistency is laborious, subjective, and error prone. For some wellbore logs, such as gamma ray and neutron porosity, borehole effects and miscalibration can cause systematic inconsistencies or errors that might be present even after the application of wellbore and environmental corrections. Biased or consistently inaccurate data in the logs can confound ML approaches into learning erroneous relationships, leading to misinterpretations, such as wrong lithology prediction, reservoir estimation, and incorrect formation markers. To overcome such difficulties, we have developed a deep learning method to provide petrophysicists with a set of consistent logs through the multiwell automatic log correction (MALC) workflow. Presently, the corrections we target are systematic errors on the standard logs, especially gamma ray and neutron logs, random noises, and to a lesser extent, local formation property misreading due to washouts. We applied the proposed method in multiple fi elds worldwide containing different challenges, and in this paper, we include the results in two fi eld examples. The first one covers the correction of synthetic coherent noise added to fi eld data, and the second example covers the correction applied to original measurements.

The Mishrif Reservoir Characteristics Utilizing Well Log Data Interpretation in the Fauqi Oilfield in Maysan, Southern Iraq

The Mishrif Formation was deemed the main oil reservoir in the Fauqi oilfield, southern Iraq. This paper aims to predict characteristic properties of the Mishrif Formation depending on the interpretation of well logs data. The data were collected from five wells of log images which had been digitized with Didger software version 5. 12. 1762 to be analyzed and interpreted using interactive petrophysics 3.5.1.2 software. The main lithology of the Mishrif Formation in this region was determined using standard (M-N xplot) based on the integration of density-neutron logs. The results confirmed that the Mishrif Formation is mainly composed of limestone units. A Gamma-ray log was used to estimate the shale volume in the Mishrif Formation depending on old rock function. As a result, shale volume relative to the bulk volume was about 19%, and an increasing volume was noticed in the upper reservoir, reaching the highest in the MA and MB11 units. The Mishrif Formation porosity was calculated using the interpretation of neutron, density, and sonic logs. The results showed that the Mishrif Formation is distinctive with low to medium porosity values (4.8 to 17%). The calculation of cores manifested the dominance of secondary porosity in the MB21 unit of the formation in comparison to the other stratigraphic units in the studied area. These results led to a the fact that the MB21 unit was more exposed to dissolution diagenetic processes in the south dome, which provided a good permeability integrated with the medium effective porosity, puttinh it as the main reservoir unit.

The Asmari Formation Petrophysical Parameters in the Abu Ghirab Oil Field of the Zagros Fold Belt Basin, Southeastern Iraq, Using Well Log Data Interpretation

The current study represents the evaluation of petrophysical characteristics, lithological analysis, and identification of the Asmari Formation for the main and sub-reservoir units in the Abu Ghirab oil field, Maysan, southeast of Iraq. This petrophysical assessment was accomplished using the interpretations of well log data to know the properties of the Asmari Formation reservoir. The available logs such as resistivity, gamma ray, neutron and density logs were converted to digital data using the Diger Sotware. The lithic properties of the reservoir like porosity, total shale saturation, oil and water saturation were interpreted and calculated using the interactive petrophysics software. The lithic and mineral study was completed and the type of mineral filling was known through the cross-plot of the porosity logs for different wells. The petrophysical properties were calculated and represented on the CPI profile. Depending on the petrophysical characteristics, the Asmari Formation was divided into main and sub units, which are: Jeribe-Euphrates and its sub-units A1, A2, and A3; Upper Kirkuk unit and B1, B2, B3, B4 sub-units; and Middle-Lower Kirkuk unit and C subunit. The results showed that the reservoir has economic oil productivity. The subunits B2, B3 in Upper Kirkuk were characterized by high effective porosity values and high hydrocarbon content, and they are the highest produced units in Upper Kirkuk in studied wells. The vary on lithology represents as dolomites in the Jeribe, Euphrates, limestones in the Kirkuk group, and the mineral components were calcite and dolomite.

Palynomorph composition and palynofacies analysis of the Cenomanian Bahariya Formation in the north Western Desert of Egypt: Depositional palaeoenvironment and sequence stratigraphic implications

Abstract This study investigates the Bahariya Formation using 39 rock-cutting samples from the Abu Gharadig Basin in the north Western Desert, Egypt. An approach combining palynomorph composition and palynofacies analysis with lithological composition and geophysical gamma ray log values is used to assess biostratigraphic age constraints, prevalent depositional palaeoenvironments, and relative sea level changes. A moderately diverse assemblage comprised mainly of terrestrial palynomorphs versus minor content of marine palynomorphs is identified. Marker spore and pollen taxa are used to name three interval zones of early Cenomanian, and early-middle Cenomanian ages. Quantitative distribution of particulate organic matter using cluster analysis reveals two palynofacies assemblages: an older assemblage dominated by phytoclasts (mainly wood particles); and a younger assemblage characterised by moderate abundances of amorphous organic matter and phytoclasts. Palynofacies analysis suggests deposition of the Bahariya Formation in fluvio-deltaic to inner neritic shelf environments. Based on the pronounced stratigraphic variations in the particulate organic matter composition, including terrestrial:marine palynomorph ratio, lithological and gamma-ray log data, three complete transgressive-regressive sequences and one incomplete sequence and their systems tracts are defined. The transgressive-regressive trends reflect 3rd order sequences. Additionally, successive changes in relative sea level indicate a continuous rise at the topmost part of the succession.

Permeability estimates of a carbonate reservoir in Campos Basin, Southeastern Brazil, using well logs with empirical, multilinear regression and machine learning approaches

Well logging records the physical properties of geological formations and the fluids traversed by the wells. This operation is interested in parameters such as lithology, hydrocarbon presence, permeability, porosity, and fluid saturation. Generally, oil reservoirs are sandstone or carbonate rocks, and the latter’s characterization is a critical question in the petrophysical properties distribution, mainly permeability. Estimating permeability is a complex task due to the heterogeneity of these reservoirs. Therefore, this work used conventional logs to estimate the permeability of wells A03 and A10, both belonging to the oilfield A, Campos Basin, Southeastern Brazil. Alongside the logs, permeability measured in the laboratory in rock samples was used to validate the achieved estimates. Thus, the estimates used basic logs as input and approaches such as Timur empirical equation, multilinear regression, and machine learning techniques, like fuzzy logic, neural network, and decision tree. Pearson's coefficient of determination R was used as the comparison metric with the experimental data. The number of samples in training was 70%, with 15% in the validation and testing, and the results show that the first four estimates showed bad fits (R£0.60), while the decision tree showed good fits (R>0.60). This approach also showed that the gamma-ray and resistivity logs are the ones that have the most significant weight in the estimates.

Prediction of reservoir brittleness from geophysical logs using machine learning algorithms

Brittleness is an important geomechanical property of reservoirs, which is usually estimated from cores or sonic logs that are expensive to acquire. In this study, we report data-driven, machine learning workflows to predict brittleness from less expensive, readily available conventional logs. We propose three strategies to predict brittleness using gamma ray, neutron porosity, density, caliper, sonic, and photoelectric factor logs by utilizing gradient boosting (GB), support vector regression (SVR), and neural networks (NN). The first strategy involves predicting brittleness directly from the logs while the second strategy predicts shear sonic logs used for the estimation of brittleness. The performance of the models given as R2 on deployment on the testing set for the first strategy is: GB (0.87), SVR (0.73), and NN (0.82), while for the second strategy: GB (0.94), SVR (0.87), and NN (0.94). In the third strategy, we convert the prediction into a classification task by grouping the brittleness estimate into ductile, transition, and brittle. The accuracy of model deployment on the testing set for the third strategy is: GB (89.37%), SVM (89.06%), and NN (89.16%). We demonstrate that depending on the strategy adopted, the gradient boosting algorithm outperforms the other peer algorithms in terms of training and validation scores. Furthermore, we combine the three algorithms using a committee machine to improve the performance of the model. The workflow in this study can be adopted to predict other reservoir properties from available logs. The workflow can also be adopted to characterize reservoir heterogeneity from seismic traces trained by well logs.

Cretaceous sequence stratigraphy of the northern Baltimore Canyon Trough: Implications for basin evolution and carbon storage

Abstract We evaluate the Cretaceous stratigraphy and carbon sequestration potential of the northern Baltimore Canyon Trough (NBCT) using >10,000 km of multi-channel seismic profiles integrated with geophysical logs, biostratigraphy, and lithology from 29 offshore wells. We identify and map six sequences resolved primarily at the stage level. Accommodation was dominated by thermal and non-thermal subsidence, though sequence boundaries correlate with regional and global sea-level changes, and the record is modified by igneous intrusion, active faulting, and changes in sediment supply and sources. Our stratigraphic maps illustrate a primary southern (central Appalachian) Early Cretaceous source that migrated northward during the Aptian and Albian. During the Cenomanian, sedimentation rates in the NBCT increased and depocenters shifted northward and landward. We show that deposition occurred in three phases: (1) earlier Cretaceous paleoenvironments were primarily terrestrial indicated by variable amplitude, chaotic seismic facies, serrated gamma logs, and heterolithic sandstones and mudstones with terrestrial microfossils; (2) the Albian to Cenomanian was dominated by deltaic paleoenvironments indicated by blocky, funnel-shaped, gamma-ray logs and clinoforms characterized by continuous high-amplitude seismic reflections with well-defined terminations; and (3) the Cenomanian and younger was marine shelf, inferred from mudstoneprone lithologies, peak gamma-ray values in well logs, and foraminiferal evidence. Long-term transgression and maximum water depths at the Cenomanian/Turonian boundary correlative with Ocean Anoxic Event 2 were followed by a regression and relative sea-level fall. We show that porous and permeable sandstones of three Aptian to Cenomanian highstand systems tracts are high-volume reservoirs for supercritical CO2 storage that are confined by overlying deep water mudstones.

Astronomical calibration of a ten‐million‐year Triassic lacustrine record in the Ordos Basin, North China

AbstractThe division of geological time is of general interest as this allows a better understanding of the geological processes and Earth's evolutionary history. Astrochronology provides a way to evaluate high‐resolution timescales of Earth's climate changes. Astronomical tuning of orbitally forced stratigraphic records to construct astronomical‐calibrated timescales has led to a progressive refinement of the geological timescale over the last decades. However, the Triassic remains a relatively weak link. A large lake with an area of 370 000 square kilometres formed in the Ordos Basin in North China during the Triassic Period. It has been demonstrated that the sedimentary deposits of the seventh Oil Member (Chang 7 Member) of the Triassic Yanchang Formation in the southern Ordos Basin responded to astronomically forced climate changes. However, the constructed astronomical‐calibrated timescales failed to reach an agreement. This study presents natural gamma ray logs and geochemical climate record of Fe and Al concentrations from the southern Ordos Basin margin that are dated precisely from 239 to 229 Ma by combined U–Pb dating and astrochronology. Therefore, the Chang 7 Member was deposited during the Ladinian and Carnian (late Middle through to earliest Late Triassic), this knowledge will refine the Triassic astronomical scale. The distribution of detrital zircon dates shows that the Chang 7 Member in the southern Ordos Basin has multiple provenance characteristics. The southern Ordos Basin is far from this provenance area, which is reflected in the low sediment accumulation rate (0.5 to 2.0 cm/kyr). In contrast to the lower part of Yanchang Formation underlying the Chang 7 Member, the large tuffs and seismites from the bottom of Chang 7 Member indicate the initiation of tectonic collision between North Qinling orogenic belt and the southern margin of the Ordos Basin.

Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China

The Triassic Yanchang Formation in the Pengyang area of Ordos Tianhuan depression is an important oil and gas formation. However, most of the oil pays in the study formations are low-resistivity or low-contrast reservoirs with low permeability, bringing challenges to the reservoir identification and evaluation by well logs. In this paper, we first measured the nuclear magnetic resonance (NMR), phase permeability, cation exchange capacity (CEC), and X-ray diffraction for core samples. Then, the genetic types for the low-resistivity pays were analyzed based on the experiment results, water analysis, and well log data collected. It was found that large variations of formation water salinity, high irreducible water saturation, and clay conductivity are the primary genetic types. Further, the random forest (RF) algorithm with sensitive parameter inputs was used to identify the oil, oil and water, and water layers. The anomaly of spontaneous potential (∆SP) that characterizes water salinity, the relative value of gamma ray log (∆GR) that describes the bound water content, resistivity, density, and acoustic logs were taken as sensitive logs according to the genetic analysis. Finally, this identification method was verified by comparison with the traditional crossplot method and oil test results. The identification accuracy of the RF is 90%, far higher than that by the crossplot method.

Case study in North Iraq: Cluster analysis of well logs using IP program

Diminish of error ratio to expecting the lithology of reservoir formation is considered as mean concerning of geology nowadays. The aim of research is to minimize the wrong reading data extract the lithology facies of the reservoir by preparing using Interactive Petrophysics program (IP) to make cluster analysis of multi well logs data. Log data of Kirkuk oil field used in this program included: Gamma ray (GR), sonic, neutron (N), bulk density (BD), Caliper and Resistivity logs. The basic concept of this method is to utilized K-mean clustering theory. Several wells have been conducted to emphasize this method which shows dropping in error ratio of confining the lithology and hydrocarbon existing as 41 wells have precisely predicted of methane prevailing.

Estimation of Internal Friction Angle for The Third Section in Zubair Oil Field: A Comparison Study

The One important rock mechanical property in geomechanical investigations is internal friction angle. In order to predict wellbore failure and construct a geomechanical earth model, friction angle is essential. It is usual to estimate the internal friction angle using core tests, but this approach requires more cores and is expensive. Instead, several empirical correlations were found to estimate the internal friction angle from logs data. The goal of this study was to determine the most accurate approach for estimating internal friction angle from log data and to demonstrate how clay volume and porosity influenced this estimate. This property is estimated using three different correlations built within Techlog 2015 software depend on neutron, density, and gamma ray logs data. The findings demonstrate that Weingarten and Perkins Weal correlation is used, especially when water production is actually occurring. The gamma ray technique does not accurately match the core data since it only employs the gamma ray log. With effective porosity and clay volume used as input data for the Plumb Clay Volume and Porosity correlation, the findings demonstrate an excellent match with the core data. The frictional angle exhibits minimal values in the Tanuma formation and at various depths as a result of a decrease in effective porosity and an increase in shale volume. The results showed that for determining frictional angle, the Plumb correlation approach was the most accurate.

Assessment of Three Non-Linear Approaches of Estimating the Shale Volume Over Yewa Field, Niger Delta, Nigeria

Accurate shale volume estimation is an important approach in reservoirs characterization as it forms the basis upon which evaluators can ascertain the hydrocarbon content of the reservoirs. The porosity, gamma ray, neutron-density and deep induction logs data were used to arrive at suitable shale volume estimates of the field studied. Analysis of well logs data was done using the TECHLOG Exploration software. Delineation of reservoirs was carried out with OpendTect software. The Microsoft excel spreadsheet was utilized to accurately estimate other suitable petrophysical parameters such as the permeability, water saturation, hydrocarbon saturation and the porosity. Three different non-linear shale volume models, the Larionov, the Steiber and the Clavier models were used to determine the reservoirs’ shale content across three wells of Yewa reservoirs characterized by varying thicknesses. Variation in the depths down hole for each of the methods revealed that shale volume estimates with the Larionov model was determined across thickness 142.646 m with top and bottom depths of 1946.605 m and 2089.252 m respectively in well Y1, thicknes 90.678 m with top and bottom depths of 2164.690 m and 2255.368 m respectively in well Y2 and thickness 107.290 m with top and bottom depths of 2303.374 m and 2410.663 m respectively in well Y3. The estimates with Steiber model were respectively determined across thicknesses 85.649 m, 95.098 m and 121.371 m for Y1, Y2 and Y3 reservoirs, and top and bottom depths of 1947.571 m and 2033.219 m in well Y1, 2041.754 m and 2136.851 m in well Y2 and 2144.979 m and 2266.442 m in well Y3 and the one with Clavier model were respectively determined across thicknesses 146.456 m, 147.752 m and 94.869 m for Y1, Y2 and Y3 reservoirs and top and bottom depths of 1760.601 m and 1907.057 m in well Y1, 1920.312 m and 2068.068 m in well Y2 and 2078.812 m and 2173.681 m in well Y3. The lowest shale volume average estimate was recorded from the Larionov model. Nevertheless, one cannot conclude that the Larionov model is the most reliable as values obtained may be because of instability in the sensitivities of utilized well logs and the complexities in the properties of wells down hole. A further investigation of the sensitivities of the well logs and the down hole properties of the wells showed that the Larionov method gives reasonable, consistent, and repetitive intervals when compared with the Steiber and the Clavier models. The Larionov model is hereby recommended for use in the study area.

Synthetic shear sonic log generation utilizing hybrid machine learning techniques

Compressional and shear sonic logs (DTC and DTS, respectively) are one of the effective means for determining petrophysical/geomechanical properties. However, the DTS log has limited availability mainly due to high acquisition costs. This study introduces a hybrid machine learning approach to generating synthetic DTS logs. Five wireline logs such as gamma ray (GR), density (RHOB), neutron porosity (NPHI), deep resistivity (Rt), and DTS logs are used as input data for three supervised-machine learning models including support vector machine for regression (SVR), deep neural network (DNN), and long short-term memory (LSTM). The hybrid machine learning model utilizes two additional techniques. First, as an unsupervised-learning approach, data clustering is integrated with general machine learning models for the purpose of improving model accuracy. All the machine learning models using the data-clustered approach show higher accuracies in predicting target (DTS) values, compared to non-clustered models. Second, particle swarm optimization (PSO) is combined with the models to determine optimal hyperparameters. The PSO algorithm proves time-effective, automated advantages as it gets feedback from previous computations so that is able to narrow down candidates for optimal hyperparameters. Compared to previous studies focusing on the performance comparison among machine learning algorithms, this study introduces an advanced approach to further improve the performance by integrating the unsupervised learning technique and PSO optimization with the general models. Based on this study result, we recommend the hybrid machine learning approach for improving the reliability and efficiency of synthetic log generation.

Carbonate fractures from conventional well log data, Kometan Formation, Northern Iraq case study

The Borehole Acoustic Televiewer, Formation Micro scanner (FMS), Formation Micro-Imager (FMI), and Electric Micro Imaging (EMI) are the most qualified approaches to fracture identification. Still, they are more expensive and only give images at specific intervals. Conventional well log tools can be identified without interval limitations. This study aimed to demonstrate the ability of the Conventional well logging tools to identify fractures in the Upper Cretaceous Kometan Formation in the Northern part of Iraq. The study area is located at latitudes 3,922,836.34 N to 3,930,557 N and longitudes 448,350.78E to 417772E for the well of Kirkuk 216 (K216) and Khabbaz 40 (Kz40), respectively. Quantitative interpretations like secondary porosity index and qualitative are the total porosity-sonic porosity cross-plot, caliper log, total gamma-ray log, and porosity logs (neutron, sonic, and density log) have been used to identify fractures in the studied formation. Some subsurface rock samples (spectral gamma ray) and outcrop observations were also used to validate well log data results. Good secondary porosity (fractures) has been Identified from the Secondary Porosity Index (SPI), which is reached a maximum value of 17%, and the average is 4% for Kz40. The maximum value is 30%, and the average is 8% for K216. Based on the qualitative interpretations of the available log data, which were validated by subsurface rock samples and observed outcrop sections of the studied formation, it has been concluded that the reservoir quality of the Kometan formation was enhanced due to high fractures in the studied wells.

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.

Obliquity-forced aquifer-eustasy during the Late Cretaceous greenhouse world

The mechanisms of short-term (105–106 years) and high-magnitude (>20 m) sea-level oscillations have long been debated involving glacio- and aquifer-eustasy, largely owing to the sparse robust evidence for the presence and significance of aquifer-eustasy, and lack of knowledge about the hydrological dynamics behind it. Non-marine/continental greenhouse archives (e.g., lake level) and their temporal correlation to marine successions (e.g., sea level) could provide clues to aquifer-eustasy. The Songliao Basin (SLB) in Northeast China is one of the largest Mesozoic terrestrial inland basins and provides a terrestrial archive of nearly all Cretaceous successions. The greenhouse late Santonian–early Campanian lower Nenjiang Formation (K2n1+2), recovered from three boreholes in the SLB, provides a unique opportunity for validating and decoding aquifer-eustasy. Initially, the cyclostratigraphy of logging gamma ray (GR) and thorium (Th) series from three boreholes was implemented, which in conjunction with precise geochronological (U/Pb zircon) ages renewed the chronology framework of the SLB. Using the astronomically tuned GR and Th datasets, the lake level of the SLB, which was recovered from sedimentary noise modeling and presented as a proxy for the water table of the groundwater reservoir, showed a clear out-of-phase relationship with the coeval sea level, validating the aquifer-eustasy hypothesis. The lake level of SLB shows prominent ∼1.2 Myr cycles and a well-coupled relationship with sea level and obliquity modulation, indicating that orbital obliquity drove the lake level and modulated water exchange between ocean and continent during the Cretaceous greenhouse period. Strong precipitation events indicated by negative excursions of ostracod δ18O correlate well with high lake levels, high obliquity, and low sea level, suggesting that more moisture was precipitated into the high-latitude continents during obliquity modulation maxima, consequently recharging the aquifer and raising the lake level while drawing down the sea level and vice versa. The close correlation between the reported marine incursion layers and the lowstand of sea level casts a doubt on the marine incursion hypothesis in the SLB; thus, more work is needed to reconcile this paradox. Overall, this study provides the first robust geological evidence for aquifer-eustasy and decodes its role in Cretaceous short-term eustasy.

Predicting sonic and density logs from drilling parameters using temporal convolutional networks

Sonic and bulk density logs are crucial inputs for many subsurface tasks including formation identification, completion design, and porosity estimation. Economic and operational concerns restrict the acquisition of these logs, meaning the overburden and sometimes entire wells are completely unlogged. In contrast, parameters that monitor drilling operations, such as weight on bit and torque, are recorded for every borehole. Previous studies have applied supervised machine learning approaches to predict these missing logs from the drilling parameters. While the results are promising, they often do not investigate the importance of different features and the corresponding practical implications. Here, we explored the feasibility of predicting compressional slowness and bulk density logs using various combinations of formation markers, gamma-ray logs, and drilling data recorded at the rig. Our tests utilized a temporal convolutional network to allow the model to learn from sequences of input features. Bayesian-based hyperparameter tuning found the optimum set of parameters for each experiment before producing the final log predictions. Finally, a permutation feature importance analysis revealed which input variables contributed most to the outputs. Although drilling parameters contain some insight into the mechanical rock properties, we found that they cannot produce the high-quality log predictions required for many tasks. Supplementing the drilling parameters with a gamma-ray log and formation data produces good-quality log predictions, with the additional inputs helping to constrain the model outputs.