Lithological Information Research Articles

Integrating a GIS-Based Multi-Influence Factors Model with Hydro-Geophysical Exploration for Groundwater Potential and Hydrogeological Assessment: A Case Study in the Karak Watershed, Northern Pakistan

The optimization of groundwater conditioning factors (GCFs), the evaluation of groundwater potential (GWpot), the hydrogeological characterization of aquifer geoelectrical properties and borehole lithological information are of great significance in the complex decision-making processes of groundwater resource management (GRM). In this study, the regional GWpot of the Karak watershed in Northern Pakistan was first evaluated by means of the multi-influence factors (MIFs) model of optimized GCFs through geoprocessing tools in geographical information system (GIS). The distribution of petrophysical properties indicated by the measured resistivity fluctuations was then generated to locally verify the GWpot, and to analyze the hydrogeological and geoelectrical characteristics of aquifers. According to the weighted overlay analysis of MIFs, GWpot map was zoned into low, medium, high and very high areas, covering 9.7% (72.3 km2), 52.4% (1307.7 km2), 31.3% (913.4 km2), and 6.6% (44.8 km2) of the study area. The GWpot accuracy sequentially depends on the classification criteria, the mean rating score, and the weights assigned to GCFs. The most influential factors are geology, lineament density, and land use/land cover followed by drainage density, slope, soil type, rainfall, elevation, and groundwater level fluctuations. The receiver operating characteristic (ROC) curve, the confusion matrix, and Kappa (K) analysis show satisfactory and consistent results and expected performances (the area under the curve value 68%, confusion matrix 68%, Kappa (K) analysis 65%). The electrical resistivity tomography (ERT) and vertical electrical sounding (VES) data interpretations reveals five regional hydrological layers (i.e., coarse gravel and sand, silty sand mixed lithology, clayey sand/fine sand, fine sand/gravel, and clayey basement). The preliminary interpretation of ERT results highlights the complexity of the hydrogeological strata and reveals that GWpot is structurally and proximately constrained in the clayey sand and silicate aquifers (sandstone), which is of significance for the determination of drilling sites, expansion of drinking water supply and irrigation in the future. Moreover, quantifying the spatial distribution of aquifer hydrogeological characteristics (such as reflection coefficient, isopach, and resistivity mapping) based on Olayinka’s basic standards, indirectly and locally verify the performance of the MIF model and ultimately determine new locations for groundwater exploitation. The combined methods of regional GWpot mapping and hydrogeological characterization, through the geospatial MIFs model and aquifer geoelectrical interpretation, respectively, facilitate decision-makers for sustainable GRM not only in the Karak watershed but also in other similar areas worldwide.

Deep learning of rock images for intelligent lithology identification

An intelligent lithology identification method is proposed based on the deep learning of rock images. The lithology information and position information in rock images can be predicted using the Faster R–CNN architecture through the RPN proposal generation algorithm and the Fast R–CNN detector. To obtain more rock features, the rock detection model is built on the ResNet structure, and the residual learning is used to retain as much as possible detailed information in the original input image. The four-step alternating training is used to fine-tuned end-to-end, and the prediction results are optimized by the cross-entropy loss and the regression loss. To speed up the model and improve the identification accuracy, data augmentation and pre-training are used to train the model. The mAP, P, R and F1 score are used as evaluation indexes of the accuracy, and the Faster R–CNN model is compared with the YOLO v4 model. Results indicate that the mAP of the rock detection model based on the Faster R–CNN is 99.19% and the F1 score is 96.6%. Compared with the YOLO v4 model, the accuracy is higher and the identification ability is more stable. The proposed rock detection model has good identification ability for different rocks in rock images, and the model is of good robustness and generalization performance, which is suitable for rapid intelligent lithology identification in practical geological and logging engineering.

Lithological classification via an improved extreme gradient boosting: A demonstration of the Chang 4+5 member, Ordos Basin, Northern China

• CRBM and PSO can be applied to improve the prediction efficiency of XGBoost. • CRBM-PSO-XGBoost is provably high-efficient for the lithology prediction. • Processing more learning samples is effective to enhance the prediction accuracy. • CRBM-PSO-XGBoost is proved to be robust even dealing with a sparse learning data. Acquiring reliable lithology information is a critical step for geological analysis since many basic jobs in early exploration have to be completed under the application of lithological materials. Lithology prediction then is always regarded as a research hotspot in geosciences. XGBoost is proved to be more powerful on pattern recognition than classic models, as it takes advantages of gradient boosting, classification tree, regularization, and other advanced machine learning techniques, thus being more potential to provide an ideal solution for lithology prediction. Nonetheless, this model is difficult to obtain optimal results due to the employment of many hyper-parameters, and will be low-efficient when dealing with many variables. Therefore, two computing techniques, continuous restricted Boltzmann machine (CRBM) and particle swarm optimization (PSO), are introduced to improve prediction performance of XGBoost. CRBM can extract fewer while more significant features from original data, and PSO will automatically optimize hyper-parameters during training process. Data used for validation is derived from tight sandstone reservoirs of member of Chang 4 + 5, western Jiyuan Oilfield, Ordos Basin, Northern China. Three experiments are designed to verify prediction capability of the proposed model. In order to highlight validation effect, two classic predictors named support vector machine (SVM) and gradient boosting decision tree (GBDT) are applied to create a contrast. The total prediction accuracy and the respective accuracy of each lithology produced by CRBM-PSO-XGBoost are all the highest in three experiments, well demonstrating the proposed model is effective to predict the lithology of tight sandstone reservoirs and has better robustness.

A 3-D geoelectric model over mineralized zone of Ugonoba, Edo State, Nigeria

The occurrence of solid minerals in Ugonoba community was investigated using the 3D electrical resistivity method. Data was acquired using PASI 16GL Terrameter using the wenner electrode configuration with a view to delineating mineral deposits in the study area. During the reconnaissance survey, the outlook of some geological features in the form of outcrops on the surface formed part of the motivation for the geophysical survey within the Ugonoba area. Ten traverses of 200 m maximum spread and 10m electrode spacing with total depth of 40.07 m were obtained in the study area to form a square grid. The acquired data was first processed and inverted using RES2DINV software to generate ten 2-D model images and later collated into 3-D using the inversion code of RES3DINV software which automatically determines a horizontal 3D depth slice, cubes and block models of resistivity distribution. These models generated were interpreted and used to ascertain the true resistivity, lithologic formation, depth extent to any buried mineral and aggregate deposited in the study area. The extracted 3D model images revealed evidence of some geological materials/minerals in the study area which fall within the high resistivity range of 2500 Ωm to 14376 Ωm. It can therefore be inferred from the standard resistivity table that the lithology of study area is composed of non-metallic type of mineral resources which are: clayey sand, lateritic clayey sand, sandstone and limestone. The estimated quantity in metric ton for the dominant lithology (sandstone, granite and limestone) is ± 10% of 1,257,142.9 which can be commercially explored.
 Keywords: Wenner-wenner array, outcrop, minerals, RES3DINV, block models.

Dolomitization geometry and reservoir quality from supervised Bayesian classification and probabilistic neural networks: Midland Basin Leonardian Wichita and Clear Fork Formations

Extensive dolomitization is prevalent in the platform and periplatform carbonates in the Lower-Middle Permian strata in the Midland and greater Permian Basin. Early workers have found that the platform and shelf-top carbonates were dolomitized, whereas slope and basinal carbonates remained calcitic, proposing a reflux dolomitization model as the possible diagenetic mechanism. More importantly, they underline that this dolomitization pattern controls the porosity and forms an updip seal. These studies are predominately conducted using well logs, cores, and outcrop analogs, and although exhibiting high resolution vertically, such determinations are laterally sparse. We have used supervised Bayesian classification and probabilistic neural networks (PNN) on a 3D seismic volume to create an estimation of the most probable distribution of dolomite and limestone within a subsurface 3D volume petrophysically constrained. Combining this lithologic information with porosity, we then illuminate the diagenetic effects on a seismic scale. We started our workflow by deriving lithology classifications from well-log crossplots of neutron porosity and acoustic impedance to determine the a priori proportions of the lithology and the probability density functions calculation for each lithology type. Then, we applied these probability distributions and a priori proportions to 3D seismic volumes of the acoustic impedance and predicted neutron porosity volume to create a lithology volume and probability volumes for each lithology type. The acoustic impedance volume was obtained by model-based poststack inversion, and the neutron porosity volume was obtained by the PNN. Our results best supported a regional reflux dolomitization model, in which the porosity is increasing from shelf to slope while the dolomitization is decreasing, but with sea-level forcing. With this study, we determined that diagenesis and the corresponding reservoir quality in these platforms and periplatform strata can be directly imaged and mapped on a seismic scale by quantitative seismic interpretation and supervised classification methods.

LITHOLOGICAL AND PALEOCOMMUNITY VARIATION ON A MISSISSIPPIAN (TOURNAISIAN) CARBONATE RAMP, MONTANA, USA

ABSTRACTThe ecological structure of ancient marine communities is impacted by the environmental gradients controlling assemblage compositions and the heterogeneous distribution of sediment types. Closely spaced, replicate sampling of fauna has been suggested to mitigate the effects of such heterogeneity and improve gradient analyses, but this technique has rarely been combined with similar sampling of lithologic data. This study analyses lithological and faunal data to determine the environmental gradients controlling the composition of Mississippian fossil assemblages of the lower Madison Group in Montana. Eighty-one lithological and faunal samples were collected from four stratigraphic columns in Montana, which represent the deep-subtidal, foreshoal, and ooid-shoal depositional environments within one third-order depositional sequence. Cluster analysis identifies three distinct lithological associations across all depositional environments—crinoid-dominated carbonates, peloidal-crinoidal carbonates, and micritic-crinoidal carbonates. Cluster analysis and nonmetric multidimensional scaling (NMS) identifies a highly diverse brachiopod biofacies and a solitary coral-dominated biofacies along an onshore-offshore gradient. Carbonate point count data and orientation of solitary corals indicate that substrate and wave energy are two potential variables that covary with the onshore-offshore gradient. Overlaying lithological information on the NMS indicates a secondary gradient reflecting oxygen that is expressed by increasing bioturbation and gradation from brown to dark gray carbonates to medium-light gray carbonates. Taken together, these findings demonstrates how combining closely spaced, replicate sampling of lithologic and faunal data enhances multivariate analyses by uncovering underlying environmental gradients that control the variation in fossil assemblages.

Field evaluation of 1.75 groundwater profiler and field screening device for on-site contamination profiling of chromium(VI) in groundwater

Groundwater contamination by toxic heavy metals pose serious health hazards to humans and the environment. The remediation of contaminated groundwater requires effective characterization of the concentrations and spatial distribution of heavy metals. Groundwater samples from long screening monitoring wells do not provide an accurate representation of the distribution of heavy metals with depth, as the data obtained from the well are an average along the screen length. This study reports the development and application of two new technologies, a direct push 1.75 groundwater profiler (GWP) that obtains groundwater samples from multiple depths in a single push, and a portable field screening device that uses novel disposable sensors to facilitate rapid, cost-effective, on-site detection of heavy metals in groundwater, surface water, and sediment pore water. Extensive fieldwork was performed at a chromium(VI)-contaminated site to evaluate and validate these two technologies for on-site contamination profiling of Cr(VI) in groundwater. A preliminary investigation was performed with an OIHPT logging tool to determine formation lithology and relative permeability for identifying potential groundwater sampling intervals and contaminant migration pathways in the subsurface. Groundwater samples from selected depth intervals were collected and analyzed by an outside laboratory analysis for total chromium and hexavalent chromium as per EPA standards 6020 (SW) and 7196, respectively. The excellent comparison of the field results with the laboratory test results confirm the potential of these technologies in obtaining high resolution site characterization data to improve the management of heavy metal-contaminated hazardous sites.

Application of machine learning models for real-time prediction of the formation lithology and tops from the drilling parameters

Lithology changes significantly affect the drilling program and the total cost of drilling an oil well, therefore, it is very important to detect the lithology variation and formation tops while drilling. Nowadays, the lithology changes are estimated from the geological data, which increases the uncertainty, especially during the exploration phase because of the data limitation.This study investigates the ability to use three machine learning models, namely, the artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and functional neural networks (FNN) to predict the lithology changes and the formation tops in real-time while drilling. These models were trained on 3162 datasets of six inputs (drilling parameters) to enable real-time prediction of the lithology changes and formation tops through four different formations of sandstone at top, followed by anhydrite, then carbonate with shale streaks, and finally carbonate formation. The optimized models were then tested on 1356 datasets gathered from a well other than the training well.The results of this study confirmed the high accuracy of the optimized ANN model which outperformed both ANFIS and FNN models in predicting the actual lithology distribution and formation tops through both training and testing wells. The ANN model predicted the different four formations considered in this study with an accuracy of more than 98.1% for training and testing data. Although the ANFIS and FNN models predicted the carbonate/shale formation top accurately for the training data, they were not able to predict the top of all formations accurately for the testing data.

Classifying Lithofacies from Textural Features in Whole Core CT-Scan Images

Summary X-ray computerized tomography (CT) is a nondestructive method of providing information about the internal composition and structure of whole core reservoir samples. In this study we propose a method to classify lithology. The novelty of this method is that it uses statistical and textural information extracted from whole core CT images in a supervised learning environment. In the proposed approaches, first-order statistical features and textural grey-level co-occurrence matrix (GLCM) features are extracted from whole core CT images. Here, two workflows are considered. In the first workflow, the extracted features are used to train a support vector machine (SVM) to classify lithofacies. In the second workflow, a principal component analysis (PCA) step is added before training with two purposes: first, to eliminate collinearity among the features and second, to investigate the amount of information needed to differentiate the analyzed images. Before extracting the statistical features, the images are preprocessed and decomposed using Haar mother wavelet decomposition schemes to enhance the texture and to acquire a set of detail images that are then used to compute the statistical features. The training data set includes lithological information obtained from core description. The approach is validated using the trained SVM and hybrid (PCA + SVM) classifiers to predict lithofacies in a set of unseen data. The obtained results show that the SVM classifier can predict some of the lithofacies with high accuracy (up to 91% recall), but it misclassifies, to some extent, similar lithofacies with similar grain size, texture, and transport properties. The SVM classifier captures the heterogeneity in the whole core CT images more accurately compared with the core description, indicating that the CT images provide additional high-resolution information not observed by manual core description. Further, the obtained prediction results add information on the similarity of the lithofacies classes. The prediction results using the hybrid classifier are worse than the SVM classifier, indicating that low-power components may contain information that is required to differentiate among various lithofacies.

Hydrochemical properties and influencing factors of nitrogen pollution for shallow groundwater in the Urban planning area of Suzhou City

Detailed study of the chemical analysis results of 14 shallow groundwater samples, representing different land use types, were carried out in an attempt to identify the hydrochemical properties and influencing factors of nitrogen pollution for the Urban planning area in Suzhou city, China. Hydrochemical properties were analyzed by water quality and its’ main influential factors of nitrogen pollution were analyzed by using ArcGIS geostatistics module and factor analysis method. The results show that the water chemistry type of shallow groundwater in the study area was mainly HCO3-Ca·Na type, the shallow groundwater was in a strong reducing geological environment rich in organic matter. Shallow groundwater shows a significant nitrogen occurrence with high ammonia and low nitrate owing to the strong reducing geological environment with abundant organic matter and low oxygen in the study area.The occurrence forms of nitrogen in shallow groundwater were controlled by the oxidation-reduction environment. Factor analysis shows that the nitrogen content and pollution of shallow groundwater are mainly controlled by human activities and geological conditions, followed by formation lithology, redox conditions and topography.

Lithological characteristics and formation conditions of lower jurassic limestones occuring in the Patil mountain (south-western Crimea)

Background. In this article, we investigate an outcrop of the Lower Jurassic limestones of the Eskiorda suite located on the northern slope of the Bodrak River basin in the south-western Crimea.Aim. The outcrop under study is mentioned in the majority of monographs devoted to the geology of the Crimean Mountains. Although the palaeontology of this assemblage has been sufficiently studied, its lithological aspects require elucidation. We carried out a series of lithological studies to identify the composition, structure and formation conditions of this limestone assemblage.Materials and methods. A macroscopic study of the outcrop was conducted followed by a detailed lithological study of collected limestone samples using a polarising microscope.Results. The conducted microscopic analysis revealed three structural types: organogenic-detrital, detrital and oolitic-organogenic-detrital limestones. The dominant component in the composition was fragments of echinoderms, mainly crinoids. Among other components were — in descending order — brachiopods, gastropods, foraminifera, remains of green algae, sponges, ostracods, ammonites and radiolarians, whose total content did not exceed 10%. Cinoid fragments were round to a various degree. The limestone fragments (intraclasts) were characterised by organogenic-clastic and pelitomorphic structures with an organogenic sludge, occasionally containing fine-sandy and silty quartz admixtures. Oolitic-organogenic-detrital limestones were almost entirely composed of crinoid fragments, some of which represented oolith cores. A specific feature of the limestones under study is their significant hardness resulting from the cementation of the structural components predominantly with crustification cement of several generations.Conclusions. The obtained results allowed us to draw conclusions about the formation conditions of the studied rocks. The limestones were formed in shallow water under the action of waves on the sediment in zones of constant currents. The hydrodynamic action on the sediment led to the formation of intraclasts. The early lithification of the sediment contributed to the formation of a hard seabed, on which subsequent generations of echinoderms settled, lived and died, forming an accumulative carbonate body.

Cased-Hole Standalone Evaluation Succeeds in the Absence of Openhole Data

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 202894, “Cased Hole Standalone Evaluation: Breaking the Barrier To Successfully Evaluate Challenging Deep Carbonate Reservoirs,” by Pradeep Menon and Carey Mills, ADNOC, and Suvodip Dasgupta, SPE, Schlumberger, et al., prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually from 9-12 November. The paper has not been peer reviewed. Accurate petrophysical evaluations (formation lithology, porosity, and water saturation) are essential in characterizing potential reservoir zones and estimating resources in place. Typically, these evaluations rely on acquisition of openhole logging measurements; however, this is not always possible. The complete paper outlines two examples from tight gas reservoirs in two separate fields offshore Abu Dhabi in which openhole data could not be acquired and petrophysical analysis was undertaken using cased-hole log data. These evaluations successfully identified gas-saturated porous intervals in each well, one of which was successfully flow-tested. Introduction A growing need exists to increase gas production in the UAE. As a result, specific gas-production targets have been mandated from development of currently undeveloped deep gas carbonate reservoirs such as the Permo-Triassic Khuff formation, the middle Jurassic Araej formation, and the Permian Pre-Khuff Unayzah and Berwarth formations. Recent appraisal wells have aimed at evaluating these reservoirs systematically by acquiring a good suite of openhole logs, cutting conventional cores, and conducting well-testing operations. These well data are combined with an evolving regional understanding to better assess and ultimately develop these complex formations. An accurate petrophysical evaluation requires the petrophysicist to develop a realistic evaluation of formation lithology, porosity, and water saturation. These parameters provide the foundations for further work such as static modeling stands, and they must be robust. The Upper Khuff is composed of dolomite occasionally grading to calcareous dolomite with minor interbeds of claystone and anhydrite. In core and cuttings, the dolomite in the uppermost section exhibits a grainstone texture with poor intercrystalline/intergranular porosity. The Lower Khuff is composed of very hard dolomite in part grading to calcareous dolomite, medium-to-dark grey-brown in places, with occasional very-fine-to-medium grainstone texture and very poor intercrystalline porosity. In this paper, the Upper and Lower Araej members are interpreted to have been deposited in an open, marine- circulation shelfal environment, while the Uweinat member is considered to have been deposited in a more- restricted circulation setting with-in a similar shelfal environment. The Barrier Openhole logging generally is the preference for formation evaluation because it represents the simplest environments and benefits from a comprehensive list of available measurements. The variety of tools and diversity of output data available make openhole log acquisition the gold standard for formation evaluation. However, in certain situations in which openhole logging is not possible because of borehole conditions (re-entry of old cased wells, wellbore instability, over-pressure), no option exists other than acquiring petrophysical data in a cased-hole environment.

INTELLIGENT STEERING DRILLING TECHNOLOGY METHOD BASED ON CLOUD BIG DATA

Steering drilling technology is one of the most important technologies in the global petroleum industry in the 21st century, and it is also a key component of horizontal drilling, the core technology of the American Shale Gas Revolution. At present, the main research goal of steering drilling is to increase the drilling speed, reduce the drilling time and risk, and intelligence is an important way to achieve this goal. The article analyzes the application of big data and artificial intelligence in the petroleum industry at home and abroad, establishes a cloud big data intelligent steering drilling method framework, proposes an artificial intelligence inversion method for logging while drilling parameters, and points out the way to realize the management of cloud big data and intelligent algorithms, and draw the following conclusions: (1)The intelligent guided drilling method based on cloud big data mainly includes the things perception layer, the big data storage layer and the cloud platform decision layer. The things perception layer realizes the collection and transmission of key information of the wellsite to the big data center. The big data storage center is mainly responsible for data storage and cloud management. The cloud platform decision layer relies on the massive data in the big data center to perform cloud ground software control, artificial intelligence decision-making, and cloud platform management. (2)Select six geophysical parameters such as SP, GR, DEN, AC, CNL, and RT, and use different Machine Learning algorithms to build models to realize the independent identification of formation lithology. The Decision Tree model and the Random Forest model have an accuracy of 0.81 and 0.89 respectively, forming a set of schemes that can quickly and automatically describe the classification of lithological characteristics. (3)The cloud platform management decision is mainly used to decode real-time upload data downhole, and obtain drilling trajectories and logging curves. The cloud artificial intelligence decision-making module performs intelligent inversion and prediction of stratum and drilling parameters, realizes intelligent correction of drilling trajectories and intelligent optimization of drilling parameters, and ensures the accuracy and speed of drilling of intelligent steering engineering.

Research on the method of dynamic PDC cutters distribution

PDC bit design, especially the design of the crown and cutters of PDC bit, is a relatively complex analysis process, which involves many factors such as the back-dip angle, cutting depth, cutting force, cutting power and other factors of PDC cutters. At present, the traditional design methods of PDC cutters distribution are mostly based on analysis of rock breaking efficiency of single cutter, which can only reflect the working state of the single cutter under the set parameters, and cannot fully reflect the differences of different positional cutters during the cutting process. In order to solve this problem, this article proposes a dynamic method through target formation lithology characteristics. The working parameters of all the PDC cutters are analyzed in detail basing on equal wear theory and bottom hole covering theory. The working parameters include static parameters (cutting position, back dip angle, cutting depth, effective cutting area.) and dynamic parameters (cutting force, cutting power, cutting torque and cutting energy etc.). The energy required by all the PDC cutters to break rock of per volume during drilling process is taken as the target constraint condition for iterative analysis. This design method can intuitively and effectively analyse the working parameters of cutters which located bit core, nose, shoulder and gauge area, and it provides a good reference for dynamic balance design of PDC bit.

Revealing Hidden Information: High-Resolution Logging-While-Drilling Slowness Measurements and Imaging Using Advanced Dual Ultrasonic Technology

A new logging-while-drilling (LWD) acoustic tool has been developed with novel ultrasonic pitch-catch and pulse-echo technologies. The tool enables both high-resolution slowness and reflectivity images, which cannot be addressed with conventional acoustic logging. Measuring formation elastic-wave properties in complex, finely layered formations is routinely attempted with sonic tools that measure slowness over a receiver array with a length of 2 ft or more depending upon the tool design. These apertures lead to processing results with similar vertical resolutions, obscuring the true slowness of any layering occurring at a finer scale. If any of these layers present significantly different elastic-wave properties than the surrounding rock, then they can play a major role in both wellbore stability and hydraulic fracturing but can be absent from geomechanical models built on routine sonic measurements. Conventional sonic tools operate in the 0.1- to 20-kHz frequency range and can deliver slowness information with approximately 1 ft or more depth of investigation. This is sufficient to investigate the far-field slowness values but makes it very challenging to evaluate the near-wellbore region where tectonic stress redistribution causes pronounced azimuthal slowness variation. This stress-induced slowness variation is important because it is also a key driver of wellbore geomechanics. Moreover, in the presence of highly laminated formations, there can be a significant azimuthal variation of slowness due to layering that is often beyond the resolution of conventional sonic tools due to their operating frequency. Finally, in horizontal wells, multiple layer slownesses are being measured simultaneously because of the depth of investigation of conventional sonic tools. This can cause significant interpretational challenges. To address these challenges, an entirely new design approach was needed. The novel pitch-catch technology operates over a wide frequency range centered at 250 kHz and contains an array of receivers having a 2-in. receiver aperture. The use of dual ultrasonic technology allows the measurement of high-resolution slowness data azimuthally as well as reflectivity and caliper images. The new LWD tool was run in both vertical and horizontal wells and directly compared with both wireline sonic and imaging tools. The inch-scale slownesses obtained show characteristic features that clearly correlate to the formation lithology and structure indicated by the images. These features are completely absent from the conventional sonic data due to its comparatively lower vertical resolution. Slowness images from the tool reflect the formation elastic-wave properties at a fine scale and show dips and lithological variations that are complementary to the data from the pulse-echo images. The physics of the measurement are discussed, along with its ability to measure near-wellbore slowness, elastic-wave properties, and stress variations. Additionally, the effect of the stress-induced, near-wellbore features seen in the slowness images and the pulse-echo images is discussed with the wireline dipole shear anisotropy processing.

COMPARAISON DES RESULTATS DE L’APPROCHE PAP/CAR ET LE MODELE USLE DANS LA CARTOGRAPHIE ET L’ESTIMATION QUALITATIVE DE L’EROSION HYDRIQUE DANS LE BASSIN VERSANT DE L’OUED AMLIL, (PRERIF - MAROC).

Este trabajo presenta los resultados de nuestro estudio sobre la cartografía y la estimación de la erosión del agua utilizando dos tipos de métodos. El primero es el del PAP/RAC 1998 que actúa en el marco del Plan de Acción para el Mediterráneo (PAM) del PNUMA, el segundo es el del modelo empírico de WISCHMEIER y SMITH 1978 (USLE).En la cuenca del Oued Amlil (153,7 km²) situada al noroeste de Taza (noreste de Marruecos) se dan todas las condiciones para que se desencadene y se acelere la erosión hídrica, las formaciones litológicas margosas blandas e impermeables (71,5%), una cobertura vegetal muy limitada (13,2%) y unas precipitaciones agresivas e intensas. El análisis de los datos naturales de la cuenca por el método PAP/CAR ha identificado cinco tipos principales de erosión (erosión muy alta 23%, erosión alta 48%, la clase de erosión significativa representa el 22%, 6% y 1% para las clases de erosión baja y muy baja respectivamente. Luego, las pérdidas de suelo fueron estimadas por el método USLE, los resultados obtenidos permiten identificar sectores a escala de cuenca donde las pérdidas tienen valores altos de 62.3% (alto riesgo entre 66 y 102 t/ha/año (34%), y muy altos (26.2%) entre 102 y 355.6 t/ha/año) This work presents the results of our study on mapping and estimating water erosion using two types of methods. The first is that of PAP/RAC 1998 acting within the framework of the UNEP Mediterranean Action Plan (MAP), the second is that of the empirical model of WISCHMEIER and SMITH 1978 (USLE). In the catchment area of the Oued Amlil (153.7 km²) located to the North-West of Taza (North-East Morocco), all the conditions for triggering and accelerating water erosion are present, soft and impermeable marl lithological formations (71.5%), very limited vegetation cover (13.2%), and aggressive and intense rainfall. The analysis of the natural data of the watershed by the PAP/CAR method has identified five main types of erosion (very high erosion 23%, high erosion 48%, notable erosion class represents 22%, 6% and 1% for low and very low erosion classes respectively. Then, soil losses were estimated by the USLE method, the results obtained allow the identification of sectors at the basin scale where the losses have high values 62.3% (high risk between 66 and 102 t/ha/year (34%), and very high (26.2%) between 102 and 355.6 t/ha/year). Le présent travail expose les résultats de notre étude concernant la cartographie et l’estimation de l’érosion hydrique selon deux types de méthodes. La première, est celle de PAP/CAR 1998 agisse dans le cadre du Plan d'action pour la Méditerranée (PAM) du PNUE, la seconde est celle du modèle empirique de WISCHMEIER et SMITH 1978 (USLE). Dans le bassin versant de l’Oued Amlil (153.7 km²) situé au Nord-Ouest de Taza (Nord-Est du Maroc), toutes les conditions de déclenchement et d’accélération de l’érosion hydrique sont présentent, des formations lithologiques marneuses tendres et imperméables (71.5%), un couvert végétal très limité (13.2%), et des précipitations agressives et intenses. L'analyse des données naturelles du bassin versant par la méthode de PAP/CAR a permis d’identifier cinq principaux types d’érosion (érosion très élevée 23%, 48% pour l’érosion élevée, la classe d’érosion notable représente 22%, 6% et 1% pour les classes d’érosion faibles et très faible respectivement. Ensuite, les pertes en sol ont été estimées par la méthode d’USLE, les résultats obtenus permettent l’identification des secteurs à l’échelle du bassin où les pertes connaissent des valeurs élevées 62.3% (risque forte entre 66 et 102 t/ha/an (34%), et très fort (26.2%) entre 102 et 355.6 t/ha/an).

Study on the intelligent identification method of formation lithology by element and gamma spectrum

The rapid and accurate identification of the formation lithology encountered during the drilling of oil and gas fields is an important step to control the trajectory of the drilling tool borehole and improve the optimal reservoir encounter rate. At present, the main way to distinguish the lithology of the formation encountered by drilling is to use artificial detection elements, which does not form a set of intelligent formation identification system. In view of the above problems, this paper proposes a method to identify the lithology of drilled formation by using element and gamma spectrum measurement and establishes a reasoning model of intelligent identification of formation lithology by using improved fuzzy clustering algorithm-SVM (IFCM-SVM) method. Field application shows that the accuracy of IFCM-SVM intelligent formation identification method proposed in this paper can reach 90.9%, and verifies the feasibility of using element and gamma spectrum measurement to realize the intelligent identification of formation lithology in drilling.

Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf

Nutrient input through submarine groundwater discharge (SGD) often plays a significant role in primary productivity and nutrient cycling in the coastal areas. Understanding relationships between SGD and topo-hydrological and geo-environmental characteristics of upstream zones is essential for sustainable development in these areas. However, these important relationships have not yet been completely explored using data-mining approaches, especially in arid and semi-arid coastal lands. Here, Landsat 8 thermal sensor data were used to identify potential sites of SGD at a regional scale. Relationships between the remotely-sensed sea surface temperature (SST) patterns and geo-environmental variables of upland watersheds were analyzed using logistic regression model for the first time. The accuracy of the predictions was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) metric. A highly accurate model, with the AUC-ROC of 96.6%, was generated. Moreover, the results indicated that the percentage of karstic lithological formation and topographic wetness index were key variables influencing SGD phenomenon and spatial distribution in the northern coastal areas of the Persian Gulf. The adopted methodology and applied metrics can be transferred to other coastal regions as a rapid assessment procedure for SGD site detection. Moreover, the results can help planners and decision-makers to develop efficient environmental management strategies and the design of comprehensive sustainable development policies.

Controls on fluvial meander‐belt thickness and sand distribution: Insights from forward stratigraphic modelling

AbstractFluvial point‐bar evolution commonly involves multiple stages of bar development driven by changes in the style of meander transformations. Complicated planform morphologies are widely recognized in remote‐sensing imagery, but the relationships between meander‐bend evolutionary behaviour and stratigraphic architecture, facies distribution, and sand volumes remain poorly understood. This study applies a geometric forward stratigraphic model (Point‐Bar Sedimentary Architecture Numerical Deduction – ‘PB‐SAND’) to simulate the internal sedimentary architecture of 24 meander‐belt segments that evolved via a broad range of meander‐bend transformation styles. Modelling inputs are constrained by channel trajectories inferred from high‐resolution Light Detection and Ranging (LiDAR) datasets, lithological information from a sedimentological database (Fluvial Architecture Knowledge Transfer System – ‘FAKTS’) and geological knowledge of trends in point‐bar lithology (for example, decrease in sand proportion with sinuosity, downstream of bend apices, and beyond the transition from point‐bar to counter‐point‐bar deposits) and in channel bathymetry (depth variations across pools and riffles). Modelling results are used to explore how the relative distribution of sand and mud is controlled by the styles of point‐bar transformation, quantified by the relative degree of meander translation versus expansion, and by the amount of bend rotation. The 24 models are classified into three groups based on cluster analysis of their mean migration angle, mean apex rotation, mean sinuosity, standard deviation of channel circular variance and preservation ratio; these quantities are known to be controlled by meander transformation types. Quantitative comparisons across these groups and relationships between metrics of planform change and quantifications of point‐bar deposits demonstrate how meander planform evolution controls point‐bar thickness and sand volume. Locally, the thickness of sand in bar deposits is controlled by the interplay of facies trends and spatial variations in bar thickness that reflect bathymetric changes, both related to local hydrodynamics. The proposed workflow establishes linkages between planform morphologies and three‐dimensional facies distributions; it can be employed to characterize the distribution of subsurface porous volumes where the planform history of meander bends can be reconstructed.

Application of brittleness index to interpret microseismic event distribution in a hydraulically fractured shale formation

Many factors can impact the location data of microseismic events, including natural fractures, rock lithology, in situ stress, and hydraulic-fracturing parameters. The distribution of microseismic events generally tends toward highly brittle areas or areas with brittle minerals. Moreover, location data of microseismic events lack effective evaluation methods. Therefore, we have developed a method to use lithologic information and prestack seismic data to explain the distribution of well Tian Xing microseismic events. We have analyzed the brittleness of the target formation through the well logs and core. We inverted the Young’s modulus and Poisson’s ratio based on simultaneous amplitude variation with offset inversion by the prestack seismic data. We then computed the 3D brittleness index (BI) property volume by Grieser and Rickman’s method. In addition, the microseismic event distribution and BI map were then combined to show the internal relationship between the two results. We found that the well logs and core analysis demonstrated that the target formation has high brittleness. Generally, areas with more natural fractures have a higher probability of inducing hydraulic fractures. However, the analysis results show that the BI has an impact on the distribution of hydraulic fractures. Therefore, BI explains the reason for the distribution of almost all events in the northeast of the perforation. These observations also supported the concept that microseismic events preferentially grow toward more brittle areas.