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Present study aims to locate potential reservoir zones in the Paleocene, Eocene and Oligocene sequences by analysing well log data of nine vertical wells in the exploration block MB-OSN-2004/1 in the Bombay offshore basin, Gulf of Cambay, India. The geophysical logs such as gamma-ray (GR), caliper (Cali), resistivity (LLD, LLS, and MSFL), photoelectric effect (PEE), neutron porosity (NPHI), and density (RHOB) are used to study the petrophysical characteristics of the identified reservoirs in this field. The relevant information from other sources like available report, mud log, and other open hole wireline log data, and local knowledge about the field are integrated with the petrophysical analysis. The cross-plots and mud log data show that the wells mainly consist of three types of lithology: shaly-sand, shale, and limestone, however, coal layers and pyrite are present at some places. Cross plots of thorium and potassium (from spectral gamma-ray log) show that the type of clay is mixed-layers of illite, montmorillonite and kaolinite in the Pipavav and Panna formations. Three of the nine drilled wells C-47-1, MTC-1, and MB-3-1 are identified by the petrophysical investigation as gas-bearing, principally in the Pipavav and Panna formations, with saturation ranging from 55% to 60% and porosity varying from 10% to 40%. Gas-bearing layers' thickness varies from 5 to 40 m and are primarily composed of clastic sediment with mixed carbonates in some locations. The remaining wells are water-bearing because the wells penetrating the silt formations are abundant in clay minerals, which keep the reservoir from releasing fluid and decrease its permeability. In addition, gas cannot rise because impervious shale layers are present in the columns of sand and calcareous sand. [ABSTRACT FROM AUTHOR]

Hydro‐geochemical characterization is challenging in dyke intruded complex geological setting. The comparison between self‐organizing map (SOM) classification and principal component analysis (PCA) is used for better understanding of hydrogeological process surrounding Amarpur dyke in Dhanbad district, Jharkhand. Total 30 water samples were collected and tested for 12 physicochemical parameters. The K‐means clustering with SOM grouped the water quality data into cluster 1 (46.67%, low mineralization), cluster 2 (36.67%, moderate mineralization) and cluster 3 (16.67%, high mineralization). The clusters of the majority of samples identified by PCA analysis is almost same as identified by SOM with little difficulty in discriminating between cluster 2 and cluster 3. The transformation of Ca‐HCO3 to Ca‐Cl‐SO4 occurred because of exchange of Ca2+ with Na+ adsorbed in the aquifer leading excess of sulphate ions. The results of this study suggest that SOM is an effective tool for a better understanding of patterns and processes driving water quality. [ABSTRACT FROM AUTHOR]

• We propose a modeling scheme for pore pressure prediction (PP). • The scheme allows understanding the role of past PP/well log to present PP. • The scheme allows getting insight into the underlying variability and uncertainty. • The scheme could be useful to identify overpressure zones in complex geology. Pore pressure (PP) study can provide insightful information about evolution history and/or geological process taking place over a region. Conventional methods, mostly are of deterministic, and they do not allow considering underlying variability and uncertainty. Here we implement Bayesian neural networks (BNN) optimized by Scaled Conjugate Gradient (SCG) and Hybrid Monte Carlo (HMC) approach to model the PP and estimate the uncertainty in prediction from well log data of well U1343E located at Bering sea slope region of the IODP Expedition 323. In the first step, to create representative samples of well log and corresponding PP samples, Eaton's and porosity methods are employed to estimate PP empirically from well log data (e.g., gamma ray, sonic velocity, bulk density and sonic derived porosity). In the second step, in total 357 representative samples are used to build a statistical model in Bayesian framework to model the PP against depth. Prior to actual data analysis, we conducted a series of experiments combining with auto-correlation function (ACF) and/or partial autocorrelation function (PACF) analysis to fix network structure (e.g., input lag, number of hidden node) and the bounds of network hyper-parameter. In contrast to previous approach, we seek to develop a mechanism which allows to explore the link between past PP and/or well log history and present PP under rapid sedimentation rate and changing environment. The model exhibits excellent performance between predicted and computed PP with Pearson's correlation coefficient, reduction of error (RE) (RE SCG-BNN ∼ 0.99; RE HMC-BNN ∼ 0.99), and index of agreement (IA) (IA SCG-BNN ∼ 0.99; IA HMC-BNN ∼ 0.98). Comparison based on coefficient of determination, R2 , it is obtained that BNN produced superior results than the conventional artificial neural networks (ANNs). Moreover, at 530 mbsf (meter below sea floor), abrupt PP change could be linked to the transition from Pliocene to Pleistocene. The approach used here, could be useful to identify overpressure zones (OPZ) and to understand the role of past PP/well log to present PP history in many other complex geo-environmental applications. [ABSTRACT FROM AUTHOR]

Accurate estimation of aquifer parameters, especially from crystalline hard rock area, assumes a special significance for management of groundwater resources. The aquifer parameters are usually estimated through pumping tests carried out on water wells. While it may be costly and time consuming for carrying out pumping tests at a number of sites, the application of geophysical methods in combination with hydro-geochemical information proves to be potential and cost effective to estimate aquifer parameters. Here a method to estimate aquifer parameters such as hydraulic conductivity, formation factor, porosity and transmissivity is presented by utilizing electrical conductivity values analysed via hydro-geochemical analysis of existing wells and the respective vertical electrical sounding (VES) points of Sindhudurg district, western Maharashtra, India. Further, prior to interpolating the distribution of aquifer parameters of the study area, variogram modelling was carried out using data driven techniques of kriging, automatic relevance determination based Bayesian neural networks (ARD-BNN) and adaptive neuro-fuzzy neural networks (ANFIS). In total, four variogram model fitting techniques such as spherical, exponential, ARD-BNN and ANFIS were compared. According to the obtained results, the spherical variogram model in interpolating transmissivity, ARD-BNN variogram model in interpolating porosity, exponential variogram model in interpolating aquifer thickness and ANFIS variogram model in interpolating hydraulic conductivity outperformed rest of the variogram models. Accordingly, the accurate aquifer parameters maps of the study area were produced by using the best variogram model. The present results suggest that there are relatively high value of hydraulic conductivity, porosity and transmissivity at Parule, Mogarne, Kudal, and Zarap, which would be useful to characterize the aquifer system over western Maharashtra. [ABSTRACT FROM AUTHOR]

A novel approach based on the concept of Bayesian neural network (BNN) has been implemented for classifying sediment boundaries using downhole log data obtained during Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. The Bayesian framework in conjunction with Markov Chain Monte Carlo (MCMC)/hybrid Monte Carlo (HMC) learning paradigm has been applied to constrain the lithology boundaries using density, density porosity, gamma ray, sonic P-wave velocity and electrical resistivity at the Hole U1344A. We have demonstrated the effectiveness of our supervised classification methodology by comparing our findings with a conventional neural network and a Bayesian neural network optimized by scaled conjugate gradient method (SCG), and tested the robustness of the algorithm in the presence of red noise in the data. The Bayesian results based on the HMC algorithm (BNN.HMC) resolve detailed finer structures at certain depths in addition to main lithology such as silty clay, diatom clayey silt and sandy silt. Our method also recovers the lithology information from a depth ranging between 615 and 655 m Wireline log Matched depth below Sea Floor of no core recovery zone. Our analyses demonstrate that the BNN based approach renders robust means for the classification of complex lithology successions at the Hole U1344A, which could be very useful for other studies and understanding the oceanic crustal inhomogeneity and structural discontinuities. [ABSTRACT FROM AUTHOR]

Electrical resistivity study assumes a special significance for mapping aquifers in hard rock areas. A two-dimensional (2D) resistivity survey of Chikotra basin, southern part of Kolhapur district in the Deccan Volcanic Province of Maharashtra was conducted. The aim of this work was to determine the aquifer zones of the study area using electrical resistivity imaging technique. The hydro-geological section derived from the available dug well/borehole lithology suggests that the top layer comprises red bole, laterite or black soil followed by weathered/fractured rock grading into compact basalts. The sources of groundwater appear to be available in weathered and fractured basalt trapped between weathered overburden and hard rock. Results from the 2D inverted models of resistivity variation with depth suggest the occurrence of aquifers mostly in weathered/fractured zones within the traps or beneath it. The resistivity models suggest that the northern part of the study area represents a promising aquifer zone with reasonable thickness of weathered basement. The models further indicate that there are several locations throughout the basin for possible groundwater exploration as it exhibited strong water-bearing potential in the subsurface rocks. [ABSTRACT FROM AUTHOR]

Summary: Inversion of vertical electrical sounding (VES) data, especially from the crystalline hard rock area, assumes a special significance for groundwater exploration. Here we used a newly developed algorithm based on the Bayesian neural network (BNN) theory combined with Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme to invert the Direct Current (DC) VES measurements obtained from 30-locations around Tenduli-Vengurla, Sindhudurg district, Maharashtra, India. The inversion results suggest that the top layer is mostly comprised of laterites followed by mixture of clay/clayey sand and garnulites/granite as basement rocks. The source of groundwater appears to be accessible in weathered/semi-weathered layer of laterite/clayey sand that exists within the depth of 10–15m from the surface. The NW–SE trending major lineaments and its criss-crosses are also identified from the apparent and true resistivity surface map. The pseudo-section at different depths in the western part of the area, near Nivti, shows extensive influence of saltwater intrusion and its impact reaching up to the depth of 30m from the surface along the coastal area. Our results also show that intrusion of saline water decreases from the western part to the eastern part of the region. Two dimensional modeling of four resistivity profiles from the study region identified two potential groundwater reservoirs; one lying between Path-Tenduli and another in between Mat and Zaraph. The deduced true electrical resistivity section against depth correlates well with available borehole lithology in the area. The results presented here would be useful for interpreting the geological signatures like fractures, major joints and lineaments, which in turn will be helpful for identifying groundwater reservoirs and drainage pattern in the crystalline hard rock area. The newly developed HMC-based BNN method is robust and would provide insights for constraining the geophysical models and criteria for modeling resistivity data. [Copyright &y& Elsevier]

The precise classification of changes in rock boundaries/facies from well-log records is a complex problem in geophysical data processing. Observed well-log data are a complex superposition of nonstationary/nonlinear signals of varying wavelengths and frequencies, shaped by the heterogeneous composition and structural variation of rock types in the earth. This impairs our ability to use traditional statistical techniques, which in most cases fail to discriminate and/or, at best, do not precisely extract facies changes from complex well-log signals. We propose a new method, set in a Bayesian neural network (BNN) framework and using a powerful hybrid Monte Carlo simulation scheme to identify facies changes from complex well-log data. We first construct a complex, composite, synthetic time series using the data from three simple models: first-order autoregressive, logistic, and random white noise. Then we attempt to identify individual signals in the pooled synthetic time series. We use the autocorrelation and the spectral characteristics of the individual signals as input vectors for training, validating, and testing the artificial neural network model. The results show that the Bayesian separation scheme provides consistently good results, with accuracy at more than 74%. When the method was tested using well-log data from the German Continental Deep Drilling Program (KTB), it was able to discriminate boundaries of lithofacies with an accuracy of approximately 92% in validation and 93% in test samples. The efficacy of the BNN in the presence of colored noise suggests that the designed network topology is robust for up to 30% correlated noise; however, adding more noise (say, 50% or more) obscures the desired signals. Our method provides a robust means for decoding finely detailed successions of lithofacies from complex well-log data, better describing the nature of the underlying inhomogeneous crust. [ABSTRACT FROM AUTHOR]

A novel approach based on the concept of super self-adapting back propagation (SSABP) neural network has been developed for classifying lithofacies boundaries from well log data. The SSABP learning paradigm has been applied to constrain the lithofacies boundaries by parameterzing three sets of well log data, that is, density, neutron porosity and gamma ray obtained from the German Continental Deep Drilling Project (KTB). A multilayer perceptron (MLP) neural networks model was generated in a supervised feed-forward mode for training the published core sample data. A total of 351 pairs of input and output examples were used for self-adaptive network learning and weight and bias values were appropriately updated during each epoch according to the gradient-descent momentum scheme. The actual data analysis suggests that the SSABP network is able to emulate the pattern of all three sets of KTB data and identify lithofacies boundaries correctly. The comparisons of the maximum likelihood geological sections with the available geological information and the existing geophysical findings over the KTB area suggest that, in addition to the known main lithofacies boundaries units, namely paragneisses, metabasites and heterogeneous series containing partly calc-silicate bearing paragneisses-metabasites and alternations of former volcano-sedimentary sequences, the SSABP neural network technique resolves more detailed finer structures embedded in bigger units at certain depths over the KTB region which seems to be of some geological significance. The efficacy of the method and stability of results was also tested in presence of different levels of coloured noise. The test results suggest that the designed network topology is considerably unwavering for up to 20 per cent correlated noise; however, adding more noise (∼50 per cent or more) degrades the results. Our analyses demonstrate that the SSABP based approach renders a robust means for the classification of complex lithofacies successions from the KTB borehole log data and thus may provide useful guide/information for understanding the crustal inhomogeneity and structural discontinuity in many other regions. [ABSTRACT FROM AUTHOR]

Modeling and analysis of gravity and earthquake data of a critically dynamic northeastern region of India assumes a special significance for understanding the seismicity and geodynamics. In this paper, the relation between lateral heterogeneity of gravity interfaces with subsurface structures, and seismic characteristics of northeast India are studied in detail to understand the seismicity and dynamics of the region using Bouguer gravity, earthquake and magnetotelluric data. The modern scaled power spectral analysis of gravity data has brought out crustal lateral heterogeneity at a depth of 8.33 km, 19.73 km and 37.14 km that correlate well with 10 km upper crust, 20 km lower crust and a Moho at 28–42 km revealed by 1D seismic crustal velocity and tomography studies. The inferred crustal depths support thinner crust over uplifted Shillong Plateau and gradual increase of depths over subduction interface of Indo Burmese Range. 2D gravity modeling constrained by seismic velocity model and 3D Euler deconvolution technique along two specific E-W and three N-S regional gravity transects delineated three significant high density anomalous heterogeneous intrusive bodies presumably due to underplating beneath Shillong Plateau, Mikir Massif and Indo Burmese Range. Based on the seismicity and stress pattern analysis through seismic ' b '-value variation, we infer that the study region exhibits compressive stress concentrations in the form of brittle deformation at shallow depths and weakening of lower crust by high density intrusive (mafic/Ultramafic) bodies of ductile nature which may cause intense seismicity besides the ongoing collision of north drifting Indian plate with Eurasia to the north and the eastern subduction interface of Indo Burmese Range. We also infer that the NE to NW trending V -shaped areas outlined north of Shillong plateau, south of Mikir massif and NE-SW trending thrust Belt of Schuppen exhibit aseismic nature and may be the future impounding seismic sources in this region. • Gravity and seismic characteristics of northeastern part of India is discussed. • Spectral analysis provide information on thrust tectonics and seismogenic nature. • Reactivation of basement faults causing diffused seismicity in shallow depths. • Imaged underplating beneath Shillong Plateau, Mikir massif and Indo Burmese Ranges. • Crustal thickness varies from 30 km to 44 km. [ABSTRACT FROM AUTHOR]