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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]

The exploration block MB_OSN-2004/1 in the Gulf of Cambay on the western coast of India is an area of interest for oil and gas exploration, as it is surrounded by hydrocarbon-producing fields including a giant field—Bombay High. However, preliminary studies indicate the presence of non-commercial hydrocarbons (mainly gas) in only three wells among the nine wells drilled in the block. We, therefore, propose an approach to reinvestigate the area by integrating rock physics modeling with multi-attribute linear regression transform to predict hydrocarbon potential from the seismic and well log data. The area of interest (~ 200 km2) is carved out of the main full 3D survey area (~ 1441 km2). We first estimated the saturation of gas with depth from the observed sonic velocity by applying rock physics modeling. Then, we applied a model-based inversion algorithm to compute the acoustic impedance volume by inverting the 3D post-stack seismic data. The computed saturation at well locations and 3D acoustic impedance were used in stepwise multi-attribute linear regression to estimate 3D saturation. The estimated gas saturation was in the range of 5–80% of the pore space within the Paleocene, Eocene, and Oligocene sequences in the Tapti, Mahuva, Pipavav, Panna, and Deccan Trap formations of the study area, where porosity varies in the range of 10–40%. This study suggests that gas can be exploited commercially from wells that penetrate sequences with good reservoir quality as so interpreted. [ABSTRACT FROM AUTHOR]

The present study demonstrates the impact of gas hydrate concentration and its morphology on pore pressure and sediment stress. We analyse the well-logging data of the 2nd Indian National Gas Hydrate Program (NGHP-02) in Area B of the Krishna Godavari Basin. Overpressure zones are delineated by observing porosity with depth and log inferred compaction trends. Eaton, Bower, Miller and porosity-based models are used for predicting pore pressure. The predicted pore pressures are ranging between ∼3 and 4.5 MPa, and ∼2.8 and 4.4 MPa respectively at Hole 02–22A and 02–23A, where the estimated fracture pressures and horizontal stresses are nearly equal to vertical stress at both holes. Within the gas hydrate stability zone (GHSZ), overpressure is not found; rather, our findings show that pore pressure falls with increasing gas hydrates saturation at both holes. This could be due to a decrease in water concentration during hydrate formation. Consequently, the effective stress of sediments increases, causing gas hydrate-filled vertical fractures to heave apart in the minimum horizontal stress directions. We observe overpressure below the gas hydrate layers at two holes, which is due to the load of the overlying sediments, low permeability created by the high concentration of gas hydrate at the bottom of GHSZ (about 40% and 80% at Hole 02–22A and 02–23A, respectively), and may be the microbial gas being produced at a faster rate than they are being expelled due to the low permeability. It is noticed that the strike directions of fractures vary with depth at both holes, which shows the anisotropic effects of stress conditions on gas hydrate-filled fractures during the time of formation. • The effect of gas hydrate concentration and morphology on pore pressure and sediment stress at sites NGHP-02-22 and 02-23. • Microbial gases are being produced at a faster rate than they are being expelled causing overpressures below BSRs. • Pore pressure drops significantly at relatively high concentrations of gas hydrates within the stability zones. [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]

A method is proposed to estimate gas hydrate saturation from three dimensional (3-D) heterogeneous model of resistivity simulated using resistivity log. Pure gas hydrates are highly resistive compared to the host sediments, and their presence in the pore space of sediments increase the resistivity of the formation. The anomalous increase of resistivity is used as a proxy for the delineation of gas hydrates using the resistivity log. A 3-D heterogeneous resistivity model has been constructed from one dimensional resistivity log in Krishna Godavari (KG) basin, eastern Indian offshore. The simulated model contains all small scale variation in resistivity of the reservoir and maintains all properties associated with covariance, like root mean square fluctuation, characteristic scales and fractal dimension of the observed log. We have estimated volumetric hydrate saturation using the three dimensional simulated model. The porosity used for estimating hydrate saturation is calculated from the simulated density field generated using the observed density log. Estimated average gas hydrate saturation is about 9.84% of the pore volume over a 1000 m × 1000 m x 131 m cubic meters area. [ABSTRACT FROM AUTHOR]

There exist several empirical and statistical methods for estimating porosity from post-stack seismic data. Here we implement and demonstrate a method for direct estimation of porosity from pre-stack seismic data. Rather than solving for acoustic impedance and then mapping those to porosity, we estimate porosity reflectivity. We demonstrate our approach with an application to 2D seismic dataset from Krishna-Godavari basin, which is inverted directly for porosity by simultaneous inversion of angle-stacks. The total porosity estimated from the density log for the depth interval of 1059–1224 m ranges from 49 to 85%. This relationship is used as an input for porosity inversion from the 2D pre-stack seismic data of gas hydrate bearing sediments. Our result images two distinct gas hydrate saturate zones in the porosity image; they are in close agreement with interpretations based on characterization of fractured filled gas hydrate saturation. The porosity along our 2D multichannel seismic section within the time interval of 1450–1615 ms ranges from 50 to 75% within the gas hydrate bearing sediments and unconsolidated sediments below the bottom simulating reflector. The water-bearing silty/clay sediments are characterized by porosity values of about 60–70%. The results demonstrate that the proposed approach is a fast and reliable method for estimating porosity directly from seismic data. [ABSTRACT FROM AUTHOR]

During the expedition 01 of the National Gas Hydrate Program (NGHP), drilling and coring were carried out at one site in the Kerala-Konkan basin on the west cost of India to validate gas hydrate. Drilling/coring results show a homogenous sequence of oozes and explains that the reflector, which was identified as a bottom-simulating reflector (BSR) on seismic section, is mainly due to changes in formation density because of less clay content in carbonate-rich sediment. Downhole logs collected at this site are of good quality and have been used to establish empirical relationship between the P-wave velocity ( V), S-wave velocity ( V), density ( ρ) and porosity ( ϕ). The established relations show very good fit with high R value (>0.73), and can be used for further studies in this region. Well known existing empirical formulas between V, V, ρ and ϕ deviate significantly from our established relations. [ABSTRACT FROM AUTHOR]

We apply machine learning techniques to classify the lithology using downhole nuclear magnetic resonance (NMR) data of gas hydrate reservoirs in the Krishna-Godavari (KG) Basin, eastern Indian offshore. We choose two sites of the second expedition of the Indian National Gas Hydrate Program (NGHP-02) for our study, one with clay dominated site NGHP-02-01 from Area E and another with silt/sand dominated site NGHP-02-05 from Area C. We obtain five distinct classes in NMR signals at two sites using unsupervised techniques such as Davies-Bouldin index, silhouette, self-organizing map, k-means clustering and emergent self-organizing map. Obtained classes are interpreted using the NMR T 2 distribution pattern with time in terms of clay, silt, sand and pebble/gravels with depth and reconciled with gamma-ray, density, porosity, resistivity, velocity and photoelectric factor. Our results demonstrate the lithology as silty-clay with minor sand at Hole NGHP-02-01A, and silt, sand with less clay and minor pebble/gravel at Hole NGHP-02-05A. The presence of gas hydrate is identified by observing an overall low amplitude T 2 distribution curve, which is due to the reduction of pore space by solid gas hydrate. The maximum concentration of gas hydrate of about 43% of pore space estimated using NMR- and density-porosity are distributed mainly in silty-clay at Hole 01A and of about 75% in clayey-silt and silty-sand at Hole 05A. Low amplitude NMR T 2 signals observed in clay-dominated sediments at higher relaxation time may be due to the fracture-filled gas hydrates at both holes. Interestingly, high porosity (90–95%), high permeability (4 mD), low bulk-density (1.2 gm/cm3) and low photoelectric factor (0.8 b/electron) are found in sandy layers within the gas hydrate-stability zone at Hole 05A, which possibly comprising buried channels. Comparison of the lithology obtained from other log data with that from NMR data and core samples illustrates that the NMR data are most effective for characterization of reservoirs, except for a high concentration of gas hydrate in a sandy/coarse-grained reservoir, which needs further studies. [Display omitted] • Machine learning techniques for classifying lithology in both clay- and sand-dominated sediments from downhole NMR T 2 curves. • Low concentration of gas hydrate is deposited in clay-dominated sediment at Hole 02-01A of Area E in the KG offshore Basin. • High concentration of gas hydrate is deposited in coarser sediment throughout the 510 m depth at Hole 02–05A in Area C. • Very loose water-bearing silty-sand layers found at Hole 02–05A may cause huge water and sand flow during the production. [ABSTRACT FROM AUTHOR]

The article provides information related to identification and quantitative assessment of Gas-hydrates along Krishna, Godavari and Mahanadi Basins in India. Gas-hydrates are crystalline substances consisting of methane and water. A research-oriented gas-hydrates program has been launched by the Ministry of Earth Sciences, Government of India which focuses on studying the impact of dissociation of gas-hydrates on environment and developing environment-safe technology.

India's National Gas Hydrate Program (NGHP) Expedition-02 was conducted in 2015 to investigate the prospective locations of sand-rich gas hydrate reservoirs along the eastern offshore, India. We select Area-B among four areas A, B, C and E of expedition NGHP-02 as it was completely unexplored during NGHP-01. Logging while Drilling (LWD) data were collected at twelve sites in area B in Krishna Godavari (KG) offshore basin, where a huge amount of gas hydrate (up to 90% of the pore volume) is present as pore-fill, fracture-fill and both pore- and fracture-filled (mixed) morphology. The resistivity log generally overestimates gas hydrate saturation because of the increase of resistivity of fresh water near gas hydrate accumulation and also in the presence of fractured-filled gas hydrate. Though gas hydrate saturation obtained from pressure cores are assumed to be ground truth but they are discrete. Hence, the application of an advanced rock physics model is required to obtain continuous quantification of gas hydrate throughout the depth considering the gas hydrate morphology and microstructure of sediment. We apply a combination of self-consistent approximation (SCA) and differential effective medium (DEM) theories to model biconnected marine gas hydrate reservoir that can deal with inherent anisotropy due to the presence of clay platelets and pore-filling, fracture-filling or mixture of both morphologies of gas hydrate. First, we determine the distribution pattern of clay platelets from the observed background velocity (water-saturated sediment) and then estimate the gas hydrate saturation at four holes of area B, where pressure core measurements are available. We predict maximum gas hydrate saturation as 50–90% of the pore space above the BSR in this area. Comparison with other rock physics model shows that prediction from the combined SCA-DEM theory matches better with the pressure core measurements. Image 1 • Rock physics modeling of clay and sand-rich reservoir incorporating anisotropy of sediment and morphology of gas hydrate. • Application of Self Consistent Approximation and Differential Effective Medium (SCA-DEM) rock physics theory. • Assessments of gas hydrate existing in pore spaces and in fractures separately from rock physics modeling. • Application of variable orientation distribution pattern of clay minerals in sediment with depth. [ABSTRACT FROM AUTHOR]

The second Indian National Gas Hydrate Program Expedition (NGHP-02) was executed in 2015 in four areas termed as Areas A, B, C and E. During this expedition, twenty five research sites were drilled and/or cored in Krishna-Godavari (KG) and Mahanadi Basins, eastern Indian offshore. During NGHP-02, zero offset vertical seismic profile (VSP) data were acquired at three sites: Sites NGHP-02-17, −19 and −22 in Area B of the KG Basin. In this study, we focus on the three sites in Area B of the KG Basin, where, zero-offset VSP and downhole acoustic log data are used to assess and characterize the gas hydrate deposits. Zero-offset VSP data are correlated with wireline log, surface seismic and synthetic seismic data to characterize and delineate gas hydrate accumulations in the KG Basin. Low velocities ranging from 1500 to 1650 m/s are observed in the unconsolidated shallow sedimentary section above gas hydrate-bearing units, whereas, very high velocities are observed in the acquired acoustic log and VSP data. In the gas hydrate-bearing sedimentary sections, the VSP derived interval velocities range from 2000 to 3000 m/s in the depth interval between 267 and 287 m below sea floor (mbsf) at Site NGHP-02-17, between 1650 and 1750 m/s in the depth interval between 306 and 366 mbsf at Site NGHP-02-19 and between 1700 and 1800 m/s in the depth interval between 198 and 290 mbsf at Site NGHP-02-22. Gas hydrates are distributed both as pore-filling and fracture-filling at all three sites, however, high concentrations are only observed as pore-filling morphology. We estimate the amount of gas hydrate considering both isotropic (pore-filling) and anisotropic (fracture-filling) acoustic reservoir models. Our estimations of gas hydrate saturation match well with the available pressure core data and suggest that high gas hydrate concentrations (to nearly 85% of pore space) are distributed in a load bearing morphology. • VSPs data acquisition, processing and analysis were presented which are acquired during NGHP-02 in KG Basin. • Gas hydrate-bearing sedimentary section were identified and characterized based on velocities derived of VSPs and acoustic log. • Synthetic seismograms were generated for Sites NGHP-02-17, 19 and 22 to correlate borehole and seismic data. • Gas Hydrate saturation using rock physics estimated from these velocities data and compares with other data. [ABSTRACT FROM AUTHOR]

Abstract: A reservoir of gas hydrates filling fractures was discovered in the Krishna–Godavari (KG) Basin during the Indian National Gas Hydrate Program (NGHP) Expedition 01 at site NGHP01-10. The existing methods for estimating gas hydrate saturation from seismic data rely on establishing an empirical relation between acoustic impedance and density porosity from well logs assuming isotropic pore-filling gas hydrate. This method, however, yields a misleading saturation for fractured clay-dominated sediments. Here we present a methodology to estimate gas hydrate saturation from seismic data based on geostatistical inversion. It integrates the verticals detail of well log data with the lateral details from seismic data to produce highly detailed estimates of gas hydrate saturation. First, gas hydrate saturation is calculated from P-wave velocities assuming anisotropic distribution at the well site. Then, probability density functions (PDFs) between acoustic impedance and the calculated gas hydrate saturation at the well site are analyzed. A Markov Chain Monte Carlo method is employed to integrate well logs with the seismic data to produce acoustic impedance. Finally, crossplots and histograms at the well site are used to estimate gas hydrate saturations along the seismic line from inverted acoustic impedance. The spatial distribution of gas hydrate varies both laterally and vertically along the line with an average saturation of 22.5%. The estimate matches reasonably with the value at the wells. [Copyright &y& Elsevier]