Borehole Logs Research Articles

Enhancing groundwater potential evaluation: Integrating borehole log data with hybrid-MCDM approach

Enhancing groundwater potential evaluation: Integrating borehole log data with hybrid-MCDM approach

Characteristics of the Albian Westgate Formation polygonal fault system

Borehole logs and 3-D seismic data are used to determine the characteristics of a polygonal fault system (PFS) in the Albian Westgate Formation in western Saskatchewan and eastern Alberta. The basal Upper Cretaceous formation is a siliceous siltstone to mudstone deposited in the Western Interior Seaway beneath the Great Plains of North America. The faulted zone is layerbound within the ~80 m thick Westgate Formation at Swift Current, Saskatchewan. Most faulting occurs along grabens approximately 125 m wide and 10 m deep. The grabens are interconnected, have random strike directions, and lengths up to 800 m. The continuous depositional environment of the Westgate Formation enables timing and compacted sediment thickness estimates for the PFS initiation. The faults appear to have started in ~15 m or more of compacted sediment within the first 270,000 years of the 1.5 Myr Westgate Formation interval. Interpreting the seismic dataset and accompanying wellbore data has resulted in a simple model that enables PFS identification using only wellbore data. Wellbore data interpretations from Alberta and Saskatchewan show the PFS identified at Swift Current may range across a large area. The implications for an extensive PFS identification in the Lower Cretaceous Westgate Formation and homotaxially equivalent Mowry Shale across central North America are discussed.

Characterizing the Namorado Sandstone in southern Brazil through the analysis of a multi-borehole database and utilizing a contemporary workflow

The petrophysical characterization of reservoirs requires the identification of sedimentary facies based on the integration of geophysical well logs and rock analyses in the laboratory, which is one of the significant challenges for the petroleum industry. In this work, laboratory analysis results were linked to quality controls of data pre-processing in a new workflow by integrating several techniques. The wells studied were zoned and laterally correlated based on electrofacies characteristic core analysis and petrographic descriptions. This integration resulted in a modeled geologic section for the Namorado Sandstone in the Campos Basin of southeastern Brazil, created based on various data from each well. The borehole NA2 was selected as the key wellbore after creating this section. The logs from this borehole were then analyzed to separate the permo-porous (reservoirs) and non-porous (non-reservoirs) horizons. The zones containing water and hydrocarbons in the permo-porous sections of interest were split along the wells. Subsequently, the identified sectors were interpreted using petrophysical techniques to confirm the preliminary categorization. After combining all quantitative products to quantify the porous layers capable of producing mobile oil, the cut-off factors were introduced. The next step was determining the parameters of the Archie equation, the amount of clay present, and the water saturation from core descriptions, which led to the discovery of deep laminated sandstones. The results strongly correlate with lithology, geological facies, borehole logs, and petrophysical laboratory data. Based on these findings, it is evident that the clay concentration is the primary factor influencing the oil saturation in this reservoir.

Seal and reservoir risk evaluation using hierarchical clustering analysis with seismic attributes in Northwestern Australia

Assessing the presence and quality of reservoir rocks and their sealing capacity is crucial for various applications, including hydrocarbon, geothermal, and CO2 sequestration projects. Typically, exploration geoscientists rely on seismic attributes and borehole logs into interpretation to integrate diverse data for estimating reservoirs and seals. However, for all seismic interpreters, the process is time-consuming.In this study, we explore the application of Hierarchical Clustering Analysis (HCA), an unsupervised machine learning technique, to streamline the integration of multidisciplinary information. While HCA and similar techniques may occasionally misclassify critical data, we demonstrate how to enhance their accuracy by carefully selecting the number of clusters and their calibration with borehole data.The novelty of our work is the innovative transformation of HCA clusters into a 3D lithology model, which can significantly facilitate the estimation of reservoir rock and seal-rock juxtaposition risk. Using the HCA clustering hierarchy, five clusters effectively discern the presence and quality of seal and reservoir rock in two different datasets. The classification, in combination with the fault probability, addresses the seal risk offshore the Northern Carnarvon Basin.

Application of Deep Learning and Optical Character Recognition Technology to Automate Classification and Database of Borehole Log for Ground Stability Investigation of Abandoned Mines

Application of Deep Learning and Optical Character Recognition Technology to Automate Classification and Database of Borehole Log for Ground Stability Investigation of Abandoned Mines

Quantitative Evaluations of Pumping-Induced Land Subsidence and Mitigation Strategies by Integrated Remote Sensing and Site-Specific Hydrogeological Observations

Land subsidence is an environmental hazard occurring gradually over time, potentially posing significant threats to the structural stability of civilian buildings and essential infrastructures. This study presented a workflow using the SBAS-PSInSAR approach to analyze surface deformation in the Choushui River Fluvial Plain (CRFP) based on Sentinel-1 SAR images and validated against precise leveling. Integrating the InSAR results with hydrogeological data, such as groundwater levels (GWLS), multilayer compactions, and borehole loggings, a straightforward model was proposed to estimate appropriate groundwater level drops to minimize further subsidence. The results showed a huge subsidence bowl centered in Yunlin, with maximal sinking at an average 60 mm/year rate. High-resolution subsidence maps enable the quantitative analyses of safety issues for Taiwan High-Speed Rail (THSR) across the areas with considerable subsidence. In addition, the analysis of hydrogeological data revealed that half of the major compaction in the study area occurred at shallow depths that mainly included the first and second aquifers. Based on a maximal subsidence control rate of 40 mm/year specified in the CRFP, the model results indicated that the groundwater level drops from wet to dry seasons needed to be maintained from 3 to 5 m for the shallowest aquifer and 4–6 m for Aquifers 3 and 4. The workflow demonstrated the compatibility of InSAR with traditional geodetic methods and the effectiveness of integrating multiple data sources to assess the complex nature of land subsidence in the CRFP.

Buckling contributes to both coseismic uplift and long-term fold growth in active fold-thrust belts

Understanding the interplay among fault slip, fold growth, and earthquakes is crucial for assessing earthquake risk and deciphering the relationship between long-term and short-term deformation in fold-thrust belts. While fault-related folding theories have dominated previous studies, the role of the buckling process—characterized by bed-parallel contraction—in seismicity and fold development has been underreported. Herein, we attempt to address this issue through an examination of the 28 March 2019 M5.0 Mangya earthquake in the Shizigou anticline, western Qaidam Basin (northern Tibetan Plateau), based on 3-D seismic imaging data, interferometric synthetic aperture radar (InSAR) analysis, and borehole loggings. We show that this earthquake was not caused by slip along known faults, but by interbed slip due to buckling in the limb of the anticline, demonstrating for the first time that buckling could result in moderate earthquakes. The buckling process not only leads to coseismic uplift deviating from the folding crest induced by slip on the underlying fault, but also modulates the long-term geometric growth of the Shizigou anticline. Our findings underscore the significance of the buckling process in contributing to short-term coseismic slip and long-term fold growth in active fold-thrust belts.

The role of fault pathways for modern and deep recharge to a semi-confined aquifer: revised analysis of modern water leakage pathways in the Davis wellfield, Memphis, Tennessee, USA

Hydrologic tracer, borehole log and geophysical data provide new insight into recharge sources and pathways to the semi-confined Memphis aquifer in the Davis wellfield, Memphis, Tennessee, U.S.A. New data and recent studies highlight the importance of faults as potential pathways for the migration of modern water into the Memphis aquifer, an important regional public, industrial, and agricultural water supply. Geochemical and environmental tracer data collected over the past 28 years, combined with hydrologic, stratigraphic, and structural data, were used to evaluate changes in water quality and different recharge pathways to the Memphis aquifer. Assessment of stratigraphic and structural relationships argue for the wellfield being bound by a fault along the bluff line (west) and another fault or faults along the eastern and southeastern margins. Modern water recharge (< 60 years old) occurs through the fault along the western boundary of the wellfield, whereas modern water and, seasonally, deep-seated (helium-bearing) fluids recharge along the fault on the southeastern margin. Each recharge source and pathway present different vulnerabilities, suggesting that the role of faults in wellfields within active tectonic regions needs to be assessed.

Contribution of the time domain electromagnetic method to the study of the Kalahari transboundary multilayered aquifer systems in Southern Angola

AbstractThe Cunene Province (Southern Angola) is facing recurrent and pluriannual droughts. Surface water supply could be reinforced using the groundwater resources of the multilayered aquifer systems (MAS) hosted in the siliciclastic sediments of the Kalahari Group. The MAS were first identified in the early 2000s in Northern Namibia and recently in the Cunene Province, by studies of the PLANAGEO project based on modern processing and reinterpretation of legacy data from the 1960s and 1970s (electrical resistivity data and deep boreholes). This article presents the results of a time domain electromagnetic (TDEM) survey conducted in the Cunene Province to: (i) contribute to the design of the hydrogeological conceptual model of the transboundary MAS, namely their geometry and extension; (ii) validate the reprocessing of the legacy data; and (iii) guide the future location of boreholes. Results depict the geometry of the sedimentary basin and the characterization of the MAS, with particular emphasis on the intermediate and deep aquifers. The borehole siting, based on the interpretation of the new TDEM data and the legacy data (clay markers in borehole logs), was successful, with a good agreement between estimated and observed horizons of the deep aquifers. However, the presence of clayey layers, a clay-rich matrix in the detrital deposits and saline/brackish groundwater led to uncertainties in the interpretation of the electrical transects. As such, recommendations are made to improve future data collection and mapping of the MAS.

Paleo-climatic control on recharge and fresh-salt groundwater distribution in the Red River delta plain, Vietnam

Paleo-climatic induced sedimentation controls present-day recharge and the fresh-salt groundwater distribution in Quaternary delta systems. During sea-level highstands, marine clays with saline pore water were deposited and are interbedded with aquifers of coarse-grained sandy fluvial and shallow marine deposits, laid down during lowstands. The low-permeable marine layers may inhibit recent recharge to deeper aquifers, and thereby limit sustainable use of these freshwater resources. This phenomenon has been investigated in the Red River delta plain, using geophysical borehole logging, transient electromagnetic soundings, groundwater chemistry, stable isotope analysis and 3H and 14C dating of groundwater. Results reveal that marine saline pore water is still present in the Holocene marine clays, implying that fresh water has not entered the clays since their deposition. Therefore, recharge within the delta plain is not occurring and the deeper aquifers are hydraulically disconnected from the upper sandy layers. Today, recharge only occurs from the hinterland. Recharge during the last glacial period has flushed saline pore water from Pleistocene marine clays, but these clays were again affected by saline water during the Holocene transgression. The use of the groundwater resources in the delta plain must be adjusted to the present recharge to be sustainable.

A comparative study of using geophysical methods for imaging subsurface voids of various sizes and at different depths

Subsurface voids pose significant geohazards, underscoring the need for their timely detection in order to mitigate the associated hazard. We report on a field study aimed at the comparative assessment of electrical resistivity tomography (ERT), seismic refraction tomography (SRT), and the multichannel analysis of surface waves (MASW) for void mapping in karstic regions. The field surveys were conducted at a site in central Texas, of typical karstic geomorphology, and involved the co-located deployment of ERT, SRT and MASW arrays. Post-surveying, boreholes were drilled at select locations for verification purposes. It is shown that MASW demonstrated limited ability to resolve voids due to its inherent theoretical limitations. In contrast, ERT revealed high-resistivity air-filled zones, and low-resistivity soil-filled regions, which aligned well with post-survey borehole logs, although deeper voids remained largely undetected. SRT clearly delineated voids through velocity reductions, but smoothing effects overestimated void velocities. Using ERT and SRT jointly provided improved void characterization compared to single-method-based interpretations, with ERT determining void type and SRT delineating boundaries. Despite the relative success of the joint ERT-SRT application, it is evident that without the corroboration provided by invasive testing, definitive void localization and characterization under arbitrary site conditions remains elusive.

Inferring fault structures and overburden depth in 3D from geophysical data using machine learning algorithms – A case study on the Fenelon gold deposit, Quebec, Canada

AbstractWe apply a machine learning approach to automatically infer two key attributes – the location of fault or shear zone structures and the thickness of the overburden – in an 18 km2 study area within and surrounding the Archean Fenelon gold deposit in Quebec, Canada. Our approach involves the inversion of carefully curated borehole lithological and structural observations truncated at 480 m below the surface, combined with magnetic and Light Detection and Ranging survey data. We take a computationally low‐cost approach in which no underlying model for geological consistency is imposed. We investigated three contrasting approaches: (1) an inferred fault model, in which the borehole observations represent a direct evaluation of the presence of fault or shear zones; (2) an inferred overburden model, using borehole observations on the overburden‐bedrock contact; (3) a model with three classes – overburden, faulted bedrock and unfaulted bedrock, which combines aspects of (1) and (2). In every case, we applied all 32 standard machine learning algorithms. We found that Bagged Trees, fine K‐nearest neighbours and weighted K‐nearest neighbour were the most successful, producing similar accuracy, sensitivity and specificity metrics. The Bagged Trees algorithm predicted fault locations with approximately 80% accuracy, 70% sensitivity and 73% specificity. Overburden thickness was predicted with 99% accuracy, 77% sensitivity and 93% specificity. Qualitatively, fault location predictions compared well to independently construct geological interpretations. Similar methods might be applicable in other areas with good borehole coverage, providing that criteria used in borehole logging are closely followed in devising classifications for the machine learning training set and might be usefully supplemented with a variety of geophysical survey data types.

A Comparison of Statistical Validity of In-Situ Hydraulic Conductivity Prediction Models of Rock Mass Inferred from Borehole Logs and Lugeon Test Data

In-situ hydraulic conductivity is a vital property in rock engineering for jointed rock mass. Understanding its correlation with rock mass parameters is crucial for water circulation. Therefore, a study was carried out to develop statistically significant empirical relationships between hydraulic conductivity and various rock mass parameters to estimate in-situ hydraulic conductivity from Lugeon test and various rock mass parameters obtained from borehole logs.The study initially aimed to establish a correlation between hydraulic conductivity and Rock Quality Designation (RQD). However, the outcomes were unsatisfactory, prompting further research. Later, two more robust models were developed, namely the HC-model and modified HC-model. The HC-model incorporated four rock mass parameters, including Rock Quality Designation Index, Depth Index, Gouge Content Designation Index, and Lithology Permeability Index, achieving a maximum coefficient of determination (R2) of 0.46. The modified HC-model included six parameters, encompassing fracture frequency and theoretical aperture, resulting in an improved R2 of 0.69. Both models significantly outperformed RQD-alone predictions (R2 &lt; 0.10), highlighting the need for incorporating multiple rock mass parameters in predicting hydraulic conductivity due to a complex interplay of various factors. However, the effects of joint persistence and roughness are limiting in the present analysis.

Application of Electrical Resistivity Sounding in delineation of the Aquifer Transmissivity and Basement Structure at Igarra, Southwestern Nigeria

Information on the aquifer transmissivity and basement structure of Igarra, southwestern Nigeria, is scarce. Thus, this study aimed to apply electrical resistivity sounding and drilled borehole information to determine the underlying bedrock structure (basement structure) and the aquifer transmissivity of Igarra. Twenty vertical electrical soundings (VES) were carried out along 4 established E-W traverses intending to intercept the fracture systems. The resistivity data acquired were curved matched, and iterated using Schlumberger O' Neil software to obtain layer parameters. The layer parameters were evaluated to obtain the Dar-`Zarrouk parameters, which were used to determine the aquifer transmissivity. The resistivity-sounding result revealed four to six geoelectric layers that comprised topsoil, lateritic soil, weathered basement, fractured basement, partially fractured basement and fresh basement. The VES result revealed an undulating basement, with depths varying from 20.1 m to 53.8 m, suggesting evidence of fracturing and faulting within the basement. Correlation of the VES data and borehole log revealed that the weathered and the fractured basement constitute the aquifers, found between 2.4 – 53.8 m depths, and aquifer thickness ranged from 0.7 – 44.3 m. Analysis of the VES result showed an average computed transmissivity value of 18.48 m2/day. These values indicate that the basement is undulating with adequate groundwater-yielding materials (aquifer), capable of promoting adequate recharge potentials from precipitation.

On the Method of temperature Measurements in Borehole Using Precision thermometers

Precise autonomous borehole thermometers (loggers) allow measuring temperature anomalies of small amplitude (up to 1 mK). It significantly expands the range of problems that borehole thermometry can solve in exploration geophysics, hydrogeology, engineering geology, and geoecology. However, thermal logging techniques remain outdated that hinders the effective implementation of the capabilities of these devices. The paper discusses methodological issues of precision temperature measurements in water-filled boreholes. Experimental studies with an autonomous thermometer rBrsolo³ T (RBR Ltd., Canada) showed that the device’s response to changes in ambient temperature (relaxation) is complex. Within a few seconds after the immersing of device in a borehole, it registers a temperature close to the fluid temperature. However, this temporary “window” closes soon, and after 15–20 seconds the measured temperature begins to deviate more and more from the undisturbed temperature. Next time measured temperature returns to the undisturbed temperature after 1000–6000 sec. In addition, the temperature response in the interval of 20–600 sec is complicated by non-periodic fluctuations with an amplitude of 0.02–0.05 K associated with thermal convection. The presence of a time “window” on the relaxation curve makes it possible to measure fluid temperature quickly and accurately in stop-and-go mode. The design parameters of thermometers determining the width of the “window” were assessed using mathematical modeling. Recommendations for conducting precision temperature logging of boreholes in both continuous and stop-and-go modes have been provided.

The importance of core for carbonate reservoir evaluation: a case study from the Barra Velha Formation, Santos Basin, Brazil

Abstract The lacustrine carbonates of the Barra Velha Formation are a prolific hydrocarbon reservoir in the Santos Basin, Brazil. In many fields, they comprise decimetre- to metre-scale cycles composed of laminated calcimudstones, spherulite and shrub-dominated facies. However, locally these cycles are replaced by decametre packages of re-worked shrub grainstone/rudstone/breccia and in situ shrub framestone with significant (&gt;30°) depositional dips. The latter could be interpreted in several ways (e.g. fault-block highs, carbonate mounds), but the integration of seismic, borehole image log and whole-core datasets converge on a model of aggrading carbonate mounds which developed in or marginal to a lake setting. The core datasets in this study demonstrate a distinctive depositional fabric within the carbonate mounds. From a production geology standpoint, the crucial difference between mound- and cyclothem-dominated successions is their permeability architecture. Cyclothem-dominated intervals show prominent and laterally continuous, decimetre-scale vertical matrix permeability variations. Mound-dominated intervals lack fine-scale palaeo-horizontal layering and exhibit a greater prevalence of irregular, centimetre-scale conduits and higher vertical permeability. This difference can only be reliably characterized via the integration of whole-core samples with other datasets and has a significant quantified impact on sweep and production performance.

Groundwater Exploration and Assessment in Arid and Semi-Arid Regions of Basaltic Terrain of Solapur: Lessons Learned and Future Prospects

This research delves into the intricate dynamics of groundwater exploration and assessment in the arid and semi-arid basaltic terrain of Solapur, India. The study investigates the hydrogeological complexities of the region, emphasizing the importance of community involvement and sustainable practices in groundwater management. Employing advanced geophysical surveys, borehole logging, and hydrogeological modeling, the research uncovers the challenges and successes encountered during the study, providing valuable insights for future exploration strategies. Key findings highlight the heterogeneity of basaltic formations, the significance of local community engagement, and the need for adaptive approaches in groundwater exploration. Lessons learned from successes in geophysical surveys and challenges faced in borehole logging contribute to the knowledge base for effective exploration techniques in similar geological settings. The research's significance for Solapur lies in its potential to guide sustainable water management practices, empower local communities, and inform policy formulation. The emphasis on community involvement, awareness, and regulatory measures serves as a foundation for addressing water scarcity challenges in the region. Beyond Solapur, the study holds broader implications for arid and semi-arid regions globally. It contributes to scientific knowledge, informing policy frameworks, and promoting community-centric approaches to groundwater management. The research provides a blueprint for addressing water resource challenges in diverse geographical contexts, ensuring the resilience of communities and the preservation of vital groundwater resources.

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.

Developing a 3D hydrostratigraphical model of the emerged part of the Pelotas Basin along the northern coast of Rio Grande do Sul state, Brazil

The coastal plain of Rio Grande do Sul state, in Brazil, is highly vulnerable to expected changes in sea level, while having an increasing population and consequently increasing water demands. Adequate management is essential to restrain contamination, depletion and salinization of the region’s aquifers considering current and future challenges, but geologic knowledge is essential to guide groundwater sustainable practices. To contribute to this discussion, this work integrated existing geological data from the northern coast of Rio Grande do Sul state to create a three-dimensional representation of the main hydrostratigraphical units of the region and its relation to the basement rocks, expanding the current knowledge of the coastal aquifer system. A review of existing data was carried out, consisting of 307 borehole logs from 13 municipalities inside the area of interest, as well as 19 vertical electrical soundings and 37 logs from oil and coal exploratory drillings, that resulted in 315 input points for the model. This work builds up on the conceptual model previously developed for the area, that defined four hydrostratigraphical units for the region, and was able to constrain the geometries of the main aquifers (unit 1 and 3) and aquitards (unit 2 and 4) and their relation to the basement rocks, showing them to be more heterogeneous in thicknesses and extent than previously thought. In addition, this work was able to model what could be a fifth hydrostratigraphical unit, that strongly differs from the other four and could be an indication of the alluvial fans previously described in the literature.

Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco

Groundwater resources in Morocco often face sustainability challenges due to increased exploitation and climate change. Specifically, the Al-Haouz-Mejjate groundwater in the Marrakesh region is faced with overexploitation and insufficient recharge. However, the complex subsurface geometries hamper hydrogeological modeling, characterization, and effective management. Reliably estimating aquifer substrate topography is critical for groundwater models but is challenged by limited direct measurements. This study develops nonlinear machine learning models to infer substrate depths by fusing sparse borehole logs with regional geospatial data. A Gaussian process regression approach provided robust holistic mapping, leveraging flexibility, and uncertainty quantification. Supplementary neural network architectures focus on isolating specific variable relationships, like surface elevation–substrate. Model accuracy exceeded 0.8 R-squared against validation boreholes. Spatial visualizations confirmed consistency across landscape transects. Elevation and piezometric data proved most predictive, though multivariate inputs were required for the lowest errors. The results highlight the power of statistical learning to extract meaningful patterns from disparate hydrological data. However, model opacity and the need for broader training datasets remain barriers. Overall, the work demonstrates advanced machine learning as a promising avenue for illuminating complex aquifer geometries essential for sustainability. Hybrid approaches that use both data-driven and physics-based methods can help solve long-standing problems with hydrogeological characterization.