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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]

The Tuaheni Landslide Complex, located on the upper slope of the northern Hikurangi Margin in New Zealand, is a unique place to research on slow slip creep-like deformation and seabed failure, as well as their possible relationship with the presence of gas hydrates, cold seeps, and fluid migration. Based on the visual interpretation of seismic data, it is sometimes very difficult to identify various subsurface structures and tectonic features. We study certain seismic attributes, namely the reflection strength, instantaneous frequency, instantaneous phase, and the Hilbert transform, in the Tuaheni Landslide Complex and observe that these attributes play a very important role in identifying and interpreting various subsurface geological features and bed boundaries that are not clearly visible in the seismic sections. In general, these seismic attributes are studied to identify hydrocarbons such as oil and gas. However, in this present study these seismic attributes nicely illustrate the fluid migration pathways, the decollement of the sediment slide, the base of the debris flow, the base of the deformed sediment and gas migration, etc., along two perpendicular seismic profiles crossing the Site U1517 of IODP Expedition 372. The instantaneous phase and Hilbert transform attribute depict the bed boundaries and discontinuities, whereas the reflection strength and instantaneous frequency attributes characterize the various strata in terms of whether they are associated with fluid at their bases. The possible role of tectonic activity and seafloor slope failure due to gas hydrate dissociation and vice versa is clearly visible through fluid-filled weak zones in the seismic attribute volumes. Gas hydrates are dissociating and BSRs are abruptly pinching out towards the seafloor due to the movement of hot fluid and free gas, enhancing seafloor sliding and local tectonic activities together. [ABSTRACT FROM AUTHOR]

Gas hydrate saturation and porosity are the two essential parameters for characterizing a gas hydrate reservoir. Generally, porosities determined at the well locations are interpolated and extrapolated over the seismic volume, which is not so appropriate to get an estimate of gas hydrate saturation. Here, we propose a method to predict both porosity and gas hydrate saturation directly by inverting seismic data using a global optimization technique known as Very Fast Simulated Annealing. Acoustic impedance, as a function of water saturation and porosity, is defined by a second‐degree polynomial equation used as a forward problem. These are used as the primary model parameters. Acoustic impedance derived from these values is then used to compute synthetic seismograms that are compared against observed traces. We demonstrate our approach on a 23.5 km two‐dimensional seismic line crossing four wells in the Krishna Godavari Basin, eastern Indian offshore. First, the proposed method is tested on well log data and then applied on post‐stack seismic data. The posterior probability density function and the correlation matrix between the model parameters (gas hydrate saturation and porosity) are calculated to measure the associated uncertainty in prediction. Inversion results illustrate that the approach can be efficiently used for the prediction of porosity and gas hydrate saturation directly from the seismic data. [ABSTRACT FROM AUTHOR]

Existing amplitude variation with offset (AVO) and slowness (AVS) theories fail to interpret observed amplitudes in terms of actual elastic properties of the media for a reflection event resulting from a stack of thin layers. We propose a method to replace the stack of thin layers with an equivalent medium of elastic properties using the Backus averaging theory. Numerical examples show considerable deviation in intercept calculated from the modified AVO and AVS theories from the conventional methods. AVS theory is preferred as it can be applied irrespective of impedance contrast in both precritical and postcritical reflections, and no spherical divergence correction is required. P and S wave velocities, density, and thickness of thin layers are determined using the inversion scheme of very fast simulated annealing (VFSA). Uncertainty in prediction is evaluated using an approximate marginal posterior probability density function and a parameter correlation matrix. We demonstrate our methodology on synthetic and real seismic data from the Sleipner field, which is a good example of enhanced seismic amplitudes due to interference among reflections from thin layers. We select 11 common depth point (CDP) gathers for amplitude analysis of four identified reflectors along a line of 3‐D seismic data acquired in 2008. Predicted mean models of CO2‐saturated sand layers at all locations show that the thickness, density, and P and S wave velocities vary from 5–7 m, 1.8–2.0 g/cm3, and 1,460–1,490 and 630–650 m/s, respectively. Our analysis suggests that the model parameters are well constrained and independent except at a few locations. Key Points: We propose a modified AVO/AVS theory to account the amplitude alteration in the presence of a seismically thin layer.Our method also aids to resolve the tuning effect due to the presence of a thin layerThe proposed method efficiently predicts the elastic properties and thickness of thin stratified layers using a VFSA inversion scheme [ABSTRACT FROM AUTHOR]

We use the unsupervised and supervised neural network methods together to predict lithology of a gas hydrate reservoir from downhole data in the Krishna–Godavari (KG) offshore basin, India. In this study, we successfully identify the host litho-units of gas hydrate and show its effects in the identification of lithology using neural network techniques, which is not reported earlier. We use well log data acquired at three holes (10A, 03A and 04A) in 2006 during the first expedition of the Indian National Gas Hydrate Program (NGHP-01). Five different logging while drilling data (e.g. density, neutron porosity, gamma ray, resistivity and sonic) are considered for the mapping of lithology and gas hydrate. In the presence of gas hydrate, the resistivity and sonic velocity of the host sediments increase significantly, whereas density, neutron porosity and gamma ray are negligibly affected. Therefore, we calculate resistivity and sonic velocity for water-saturated sediment (without gas hydrate) theoretically to remove the effects of gas hydrate. At first, we apply the seven unsupervised classification methods (i.e. elbow, dendrogram, K -means, 3-D clustering, principal component analysis, Devies–Bouldin index and self-organizing map) to the data with gas hydrate (e.g. observed) and without gas hydrate (i.e. water-saturated/theoretical) to assess the data dimensionality and the number of clusters/litho-units. Each of the unsupervised schemes has its own pros and cons, and may provide different number of cluster/litho-units; sometimes, it is difficult to interpret from only one method. However, all seven methods provide same number of clusters in our study. Then, we apply the supervised classification method (i.e. Bayesian neural networks optimized by hybrid Monte Carlo searching technique) to the training data to refine the defined litho-units and map them with depth. Our approach identifies four types of litho-units and illustrates that the lithology in this area is dominated by clay (∼64 per cent) with some amount of silty clay, silt and minor sand. Gas hydrate is found in clay, silty clay and silt and not in sand. Results also show that, if gas hydrate is not considered as a separate unit, it is distributed as lithology in its hosts (i.e. clay, silty clay and silt). The method is very stable up to ∼15 per cent of random noise added to the data and results are well matched with the analysis of recovered core data. Identified lithologies at three wells correlate very well with seismic section crossing the wells. Very low permeability (<0.1 mD) estimated at three wells also indicates the clay-dominated lithology in our study area. [ABSTRACT FROM AUTHOR]

Abstract: Existence of gas-hydrate in the marine sediments elevates both the P- and S-wave seismic velocities, whereas even a small amount of underlying free-gas decreases the P-wave velocity considerably and the S-wave velocity remains almost unaffected. Study of both P- and S-wave seismic velocities or their ratio (V P/V S) for the hydrate-bearing sediment provides more information than that obtained by the P- or S-wave velocity alone for the quantitative assessment of gas-hydrate. We estimate the P- and S-wave seismic velocities across a BSR (interface between gas-hydrate and free-gas bearing sediments) using the travel time inversion followed by a constrained AVA modeling of multi channel seismic (MCS) data at two locations in the Makran accretionary prism. Using this V P/V S ratio, we then quantify the amount of gas-hydrate and free-gas based on two rock-physics models. The result shows an estimate of 12–14.5% gas-hydrate and 4.5–5.5% free-gas of the pore volume based on first model, and 13–20% gas-hydrate and 3–3.5% free-gas of the pore volume based on the second model, respectively. [Copyright &y& Elsevier]