Estimate Gas Hydrate Saturation Research Articles

Rock Physics Modelling for Estimation of Gas Hydrate Saturation Using NGHP-02 Well Data in the Krishna–Godavari Basin

Rock Physics Modelling for Estimation of Gas Hydrate Saturation Using NGHP-02 Well Data in the Krishna–Godavari Basin

Well-Logging Constraints on Gas Hydrate Saturation in Unconsolidated Fine-Grained Reservoirs in the Northern South China Sea

Recently, drilling wells have encountered rich gas hydrates in fine-grained sediments in the northern South China Sea. Gas hydrate in fine-grained sediments is very heterogeneous, and its physical properties are different from those of oil and gas reservoirs. The reliability of the classical logging saturation evaluation models established for diagenetic reservoirs is questionable. This study used four wells in GMGS3 and GMGS4 to evaluate the effects of the application of three typical methods for evaluating saturation with different principles in the unconsolidated fine-grained sediments: nuclear magnetic logging, sigma logging, and the Archie formula. It was found that the value of the lithologic capture cross-section in sigma logging and the rock’s electrical parameters in the Archie formula affect the accuracy of the model. Therefore, to obtain a reliable saturation value for fine-grained sediments, an innovative method for the calculation of resistivity and acoustic time is proposed to estimate gas hydrate saturation based on logging data, which is most consistent with the results of core analysis. The overall relative error of the verification well was 5.87%, whereas that of the density NMR logging method was 56%, showing that the accuracy of the newly proposed resistivity DT logging method’s saturation formula was significantly improved. Finally, a new model-based cross chart was developed, which can rapidly differentiate gas saturation during drilling.

Petro-physics Analysis and Rock Physics Modeling for Estimation of Gas Hydrate Saturation: A Case Study in the Mahanadi Offshore Basin

Abstract Gas hydrate saturation (Sh) is the most challenging key parameter of petrophysical evaluations for reservoir characterization. In total three wells were used (namely, NGHP-01-19, NGHP-01-09, and NGHP-01-08) to compute the petrophysical parameters in the gas hydrate-bearing sediments of the Mahanadi offshore basin. Initially, effective porosity and volume of shale are computed using conventional log data varying from 11-36% and 55-75% respectively. The most prominent technique responding to the physical property has been used to compute the Sh by using Archie’s empirical electrical resistivity method. The presence of gas hydrate, free gas, and other lithology affects the sonic velocity which is widely used to quantify the hydrate saturation. Therefore,an attempt is made to quantify the Sh from the wellbore sonic velocity using a rock physics model at higher porosity (~62%) for unconsolidated marine sediments below the seafloor. The model is best suited for gas hydrate zones where velocity increases with the hydrate saturation but underestimates the saturation for free gas zones below the bottom simulating reflector. In the study area,Sh ranges from 5-13% in the depth interval of 175-200 mbsf for well NGHP-01-19 whereas, small amount of 3-10%and 2-8% for well NGHP-01-09 and NGHP-01-08 respectively. The Sh obtained from the rock physics model is insignificantly mismatched with the saturation obtained from temperature and resistivity data due to the presence of an isotropic layer with fracture filling sediments having anisotropic properties.

Gas Hydrate Saturation Estimates, Gas Hydrate Occurrence, and Reservoir Characteristics Based on Well Log Data from the Hydrate-01 Stratigraphic Test Well, Alaska North Slope

The Hydrate-01 Stratigraphic Test Well was drilled at the Kuparuk 7-11-12 site on the Alaska North Slope in December 2018. Sonic log data provide compressional (P) and shear (S) slowness from which we determine gas hydrate saturation (Sgh) estimates using effective medium theory. The sonic Sgh estimates compare favorably with Sgh estimated from resistivity and nuclear magnetic resonance (NMR) logs, showing that gas hydrate occupies up to approximately 90% of the pore space in the target reservoir sands. The informally named B1 sand (2294 feet below mean sea level) shows lower VP/VS ratios than the D1 sand (2770 feet below mean sea level), with the lower part of the B1 sand showing lower VP/VS ratios than the upper part of the B1 sand. This corresponds to a stiffer, or more “cemented”, behavior for the lower B1 sand and less cemented behavior for the D1 sand. This trend could be due to differences in the reservoirs themselves or in the gas hydrate morphology or to both factors. We observe that the presence of gas hydrate in the upper B1 sand has greater impact on hydraulic permeability (measurements suggest a greater difference between intrinsic and effective permeability) than in the D1 sand, possibly related to gas hydrate morphology but more likely due simply to higher gas hydrate saturations in the upper B1 sand. Analyses of Sgh relative to porosity, shale fraction, and intrinsic permeability show that reservoir quality (as represented by these three metrics) exerts control on gas hydrate saturation. Grain size and mineralogy data show somewhat smaller grains and better sorting in the D1 reservoir relative to the upper B1 reservoir and smaller grains and greater clay fraction in the lower B1 reservoir relative to the other two reservoir zones. Together, these data suggest that reservoir characteristics play a role in the observed VP/VS patterns, but gas hydrate morphology (possibly varying with saturation) must also be considered.

Estimation of porosity and gas hydrate saturation by inverting 2D seismic data using very fast simulated Annealing in the Krishna Godavari offshore basin, India

ABSTRACTGas 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.

Current states of well-logging evaluation of deep-sea gas hydrate-bearing sediments by the international scientific ocean drilling (DSDP/ODP/IODP) programs

Since deep-sea gas hydrate-bearing sediments were drilled for the first time in the Blake Ridge in 1970, gas hydrates have been discovered at 53 drill sites in the continental margins of global oceans with international scientific ocean drilling (DSDP/ODP/IODP Programs). As a result, massive amounts of geophysical well-logging data have been accumulated, which provide critical information for understanding the in-situ properties of gas hydrates and their host sediments. Gas hydrates have such physical and chemical properties as non-conductivity, low density, high acoustic velocity, and high hydrogen content, which form the basis of identifying gas hydrate reservoirs and predicting their distribution by well-logging data. A series of well-logging evaluation methods have been proposed to estimate gas hydrate saturation of sediments, including Archie equation, combined methods of density and nuclear magnetic resonance well logging, various forms of three-phase acoustic wave equations, and elastic wave velocity simulations based on different rock physical models. The distribution of gas hydrates is highly heterogeneous, which is mainly manifested in the selectivity of hydrate occurrence to the lithology of host sediments and to the nucleation sites within a host sediment of the same lithology. The scientific-ocean-drilling well logging data have also been preliminarily used for evaluating the heterogeneity of gas hydrate distribution and inferring the growth habit of gas hydrates in host sediments. Nevertheless, there still exist some problems. The formation models used in logging evaluation are in general oversimplified, in which only two or three stratal components are involved. The application of high-resolution logging while-drilling (LWD) data remains limited. Log interpretation is not closely integrated with core geology. Therefore, joint inversion of lithologic components, porosity and gas hydrate saturation based on more complex formation models, together with the applications of high-resolution LWD logging data and core calibration, may represent an important direction in future well-logging evaluation of gas hydrate reservoirs.

Identification of gas hydrate based on velocity cross plot analysis

Gas hydrate is regarded as a kind of important energy resource in the recent decades. The identification of gas hydrate is a hot issue for researchers all over the world in order to exploration and exploitation. Previous research shows some methods to identify gas hydrate, but these methods also have shortcomings in different actual conditions. It is still a difficult problem to distinguish gas hydrate accurately from marine sediments. Here we propose a cross plot analysis of velocity to identify gas hydrate. Firstly, we simulate different minerals bearing sediments by rock physics models to obtain different physical properties. After a series of experiments, the results show that there are obvious differences between gas hydrate bearing sediments and other minerals bearing sediments in P-wave and S-wave velocity. Therefore, the cross plot of velocity can be used to distinguish gas hydrate qualitatively. Finally, we verify the cross plot method using the data in the Ocean Drilling Program (ODP) Leg 204 expedition of the Hydrate Ridge along the Oregon continental margin, where gas hydrate is confirmed via logging. The hydrate identification results of actual velocity data by cross plot analysis are generally in keeping with the results of core samples. Moreover, using the cross plot method also can estimate gas hydrate saturation range, which is in accordance with the measurement of chloride (Cl−) concentration in most cases.

Assessment of gas hydrate using prestack seismic inversion in the Mahanadi Basin, offshore eastern India

The Indian National Gas Hydrate Program Expedition-01 in 2006 has discovered gas hydrate in the Mahanadi offshore basin along the eastern Indian margin. However, well-log analysis, pressure core measurements, and infrared anomalies reveal that gas hydrates are distributed as disseminated within the fine-grained sediment, unlike massive gas hydrate deposits in the Krishna-Godavari Basin. The 2D multichannel seismic section, which crosses holes NGHP-01-9A and 19B located at approximately 24 km apart, indicates a continuous bottom-simulating reflector (BSR) along it. We aim to investigate the prospect of gas hydrate accumulation in this area by integrating well-log analysis and seismic methods with rock-physics modeling. First, we estimate gas hydrate saturation at these two holes from the observed impedance using the three-phase Biot-type equation. Then, we establish a linear relationship between the gas hydrate saturation and the impedance contrast with respect to the water-saturated sediment. Using this established relation and impedance obtained from prestack inversion of seismic data, we produce a 2D gas hydrate-distribution image over the entire seismic section. The gas hydrate saturation estimated from resistivity and sonic data at well locations varies within 0%–15%, which agrees well with the available pressure core measurements at hole 19. However, the 2D map of gas hydrate distribution obtained from our method indicates that the maximum gas hydrate saturation is approximately 40% just above the BSR between the common-depth points of 1450 and 2850. The presence of gas-charged sediments below the BSR is one of the reasons for the strong BSR observed in the seismic section, which is depicted as low impedance in the inverted impedance section. Closed sedimentary structures above the BSR are probably obstructing the movements of free-gas upslope, for which we do not see the presence of gas hydrate throughout the seismic section above the BSR.

Influence of pore structural properties on gas hydrate saturation and permeability: A coupled pore-scale modelling and X-ray computed tomography method

Gas hydrate, as an alternate hydrocarbon source, has attracted significant attention in past decades. A precise estimation of permeability of the gas hydrate-bearing formation is essential for predicting the flow behaviors and the associated gas production performance. In this research, the influence of gas hydrate saturation on pore structural properties and then affect permeability was investigated using a three-dimensional micro X-ray computed tomography dataset that records an experiment of xenon hydrate formation in a sand pack at selected times during the experiment. Unlike the previous work, the goal of this work is to characterize pore space evolution, during gas hydrate formation, using a set of key microscopic pore characteristics, i.e. pore and throat radii, pore throat ratio, coordination number and tortuosity, by applying pore network analysis, on larger and therefore more representative sub-volumes. The same segmented volume of that dataset was used in this work to estimate gas hydrate saturation and permeability, recalculated by the lattice Boltzmann method, on those larger sub-volumes at selected snapshots. Besides, the analysis provides further insights into the links of gas hydrate localities and local pore characteristics and therefore their controls on the permeability. New evidence of semi-quantitative nature emerges that grain-coating gas hydrate formed at low gas hydrate saturations play a crucial role on reducing permeability, while pore-filling and/or cementing gas hydrate become dominating at high gas hydrate saturations, and these can be explained by gas hydrate formation mechanisms.

Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network

Gas hydrate saturation is an important index for evaluating gas hydrate reservoirs, and well logs are an effective method for estimating gas hydrate saturation. To use well logs better to estimate gas hydrate saturation, and to establish the deep internal connections and laws of the data, we propose a method of using deep learning technology to estimate gas hydrate saturation from well logs. Considering that well logs have sequential characteristics, we used the long short-term memory (LSTM) recurrent neural network to predict the gas hydrate saturation from the well logs of two sites in the Shenhu area, South China Sea. By constructing an LSTM recurrent layer and two fully connected layers at one site, we used resistivity and acoustic velocity logs that were sensitive to gas hydrate as input. We used the gas hydrate saturation calculated by the chloride concentration of the pore water as output to train the LSTM network. We achieved a good training result. Applying the trained LSTM recurrent neural network to another site in the same area achieved good prediction of gas hydrate saturation, showing the unique advantages of deep learning technology in gas hydrate saturation estimation.

Prediction of gas hydrate saturation using machine learning and optimal set of well-logs

Resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well-specific calibration of empirical exponents in the electrical resistivity method, (ii) assumption of known pore morphology for gas hydrates in the acoustic velocity method, and (iii) presence of unknown mineralogy and bulk modulus terms in the acoustic velocity method. NMR-density porosity-derived gas hydrate saturation based on the analysis of the transverse magnetization relaxation time (T2) is considered the most precise method, but acquisition of NMR-based logs is limited at relatively recent drilled sites; additionally, its use in conventional oil and gas reservoirs is not that common due to higher cost and operational deployment limitations associated with acquiring NMR well-logs. This study proposes a new method that predicts gas hydrate saturation (Sh) for any well using porosity, bulk density, and compressional wave (P wave) velocity well-logs with neural network (or stochastic gradient descent regression) without any well-specific calibration and/or other aforementioned shortcomings of the existing methods. The method is developed by examining the underlying dependency between Sh and different combinations of well-logs, chosen from 6 routine logs, with 12 different machine learning (ML) algorithms. The accuracy of the proposed method in predicting Sh is ~ 84%, which is better than the accuracy of seismic and electrical resistivity methods (≤ 75%) per the results reported by three different studies. The robustness of the method in the specific case of permafrost-associated gas hydrates is demonstrated with well-log data from two wells drilled on the Alaska North Slope.

Gas hydrate saturation estimates in the Muli permafrost area considering Bayesian discriminant functions

In reservoir evaluation, it is essential to estimate the saturation levels of natural gas hydrates. The conventional algorithms of gas hydrate saturation based on geological experience or physical equations are simple multi-physical models that often provide varying results in the absence of core testing, and more complicated is to consider the complexity and instability of gas hydrates reservoirs in permafrost areas. Based on Bayesian statistical theory and three reasonable assumptions of the ideal saturation model, we combined a Bayesian discriminant function and a hyperplane equation to derive and verify two new saturation algorithms: the Saturation algorithm from the Bayesian discriminant function considering a linear correlation (SBDF) and the Saturation algorithm from the Bayesian discriminant function considering the conventional saturation (SBDF-CS). In experiments based on theoretical model data and field logging data from the sandstone and mudstone layers in the Muli permafrost area, the SBDF and SBDF-CS algorithms yielded maximum average saturation error less than 11.1% and 12.2%, only through logging data without core test calibration. The SBDF algorithms rapidly and objectively utilize representative sample data with multi-physical signals. The SBDF algorithm can be used for the initial saturation estimation of gas hydrate in permafrost areas, and the SBDF-CS algorithm is more competent for the accurate calculation of saturation in the comprehensive research.

Analysis of marine controlled source electromagnetic data for the assessment of gas hydrates in the Danube deep-sea fan, Black Sea

Marine controlled source electromagnetic (CSEM) data have been analyzed as part of a larger interdisciplinary field study to reveal the distribution and concentration of gas hydrates and free gas in two working areas (WAs) in the offshore Danube fan in the western Black Sea. The areas are located in the Bulgarian sector in about 1500 m water depth (WA1) and in the Romanian sector in about 650 m water depth (WA2). Both areas are characterized by channel levee systems and wide spread occurrences of multiple bottom simulating reflections (BSRs) suggesting the presence of gas hydrates. Electrical resistivity models have been derived from two-dimensional (2D) inversions of inline CSEM data using a seafloor-towed electric dipole-dipole system. Comparing the resistivity models with coincident reflection seismic profiles reveals insight in the sediment stratigraphy of the gas hydrate stability zone (GHSZ). Gas hydrate and free gas saturation estimates have been derived with a stochastic approach of Archie's relationship considering uncertainties in the input parameters available from drilling with the MeBo-200 seafloor rig in WA2.The resistivity models generally reflect the transition of marine to lacustrine conditions expressed by a sharp decay of pore water salinities in the top 30–40 m below seafloor caused by freshwater phases of the Black Sea due to sea level low stands in the past. In WA1, we derived saturation estimates of 10–20% within a 100 m thick layer at around 50 m depth below the channel which compares well with estimates from seismic P-wave velocities. The layer extends below the western levee with even higher saturations of 20–30%, but high gas hydrate saturations are unlikely within the fine grained, clayey sediment section, and the high resistivities may reflect different lithologies of lower permeability and porosity. The resistive layer terminates below the eastern levee where increasing resistivities at depth towards a stack of multiple BSRs indicate gas hydrate and free gas concentrations in the order of 10% to locally 30%. WA2 is characterized by a major slope failure at the landward edge of the gas hydrate stability field next to the channel. Gas hydrate saturation estimates within the slump area are close to zero within the GHSZ which is in agreement with coring results of the nearby MeBo drill sites. Elevated resistivities below the steeply upward bending BSR lead to saturation estimates less than 10% of free gas that may have accumulated.

Joint interpretation of geophysical field experiments in the danube deep-sea fan, Black Sea

Gas hydrates are naturally-occurring solid compounds of gas and water within almost all sediment-rich continental margins. Due to the large amounts of methane stored in submarine gas hydrates, they might serve as future reservoirs for offshore marine gas production. Assessing the reservoir characteristics requires reliable estimates of both the gas and gas hydrate concentration, which can be best addressed using geophysical and geological investigations. Here, we demonstrate the power of joint interpretation of interdisciplinary geophysical techniques and geological laboratory experiments. Regional 2D multichannel seismic data provide the broad overview of a hydrate-bearing area. High-resolution 2D and 3D seismic reflection data provide detailed images of two working areas, the buried S1 channel-levee system at 1500 m water depth (well within the gas hydrate stability zone) and a slope failure location, located at 665 m water depth (top limit of the hydrate formation) next to the S2 channel. Detailed compressional and shear wave (Vs) velocity-depth models were derived from four component ocean-bottom seismic data, the latter from P- to S-conversion upon reflection. Due to their steep reflection angles, shear wave events result in less resolved Vs models. Nevertheless, in case of a change in elasticity of the sediment matrix due to gas hydrate cementation, shear wave events can be used as an indicator. As such, Vs can give insight into the nature of hydrate formation throughout the GHSZ. We present new developments in the application of common reflection surface, normal-incidence-point tomography and full waveform inversion techniques to enhance model resolution for the seismic data sets. 2D and 3D controlled-source electromagnetic measurements provide volume information of the resistivity-depth distribution models. Electrical resistivity of the sediment formation depends on its porosity and the resistivity of the pore fluid. Gas hydrate and free gas generally have much higher electrical resistivities than saline pore fluid, and can be assessed using empirical relationships if the porosity and pore fluid salinity are known. Calibration with logging data, laboratory experiments on hydrate- or ice-bearing sediments, and resulting velocity and resistivity values, guide the joint interpretation into more accurate saturation estimations. Beyond that, a joint inversion framework supporting forward calculation of specialized geophysical methods at distributed locations is under development.In this paper, we summarize these individual components of a multi-parameter study, and their joint application to investigate gas hydrate systems, their equilibrium conditions and preservation of bottom-simulating-reflectors. We analyze data from two working areas at different locations and depth levels along the slope of the Danube Fan, which are both characterized by multiple bottom simulating reflectors indicating the presence of gas hydrate. In the first working area we located two depth windows with indications for moderate 16%–24% gas hydrate formation, but no vertical gas migration. In the second working area we observed fluid migration pathways and active gas seepage, limiting gas hydrate formation to less than 10% at the BSR. Some discrepancies remain between seismic-based and electromagnetic-based models of gas and gas hydrate distribution and saturation estimates, indicating that further in-situ investigations are likely required to better understand the gas hydrate systems at our study areas and to calibrate the inversion processes, which will be required for a joint inversion framework as well.

Dielectric behaviors of marine sediments for reliable estimation of gas hydrate saturation based on numerical simulation

The accurate evaluation of hydrate saturation, in marine sediments, not only can provide references for hydrate resources assessment but also help to monitor hydrate decomposition that might cause disasters. The dielectric properties can be employed to efficiently estimate hydrate saturation. However, the correlations between dielectric properties and hydrate saturation are still poorly understood. To obtain such a reliable relationship, we first adopt a numerical simulation method based on the three-dimensional finite difference model and digital core technology to calculate the dielectric properties of hydrate-bearing marine sediments. After testing and validating the numerical method by comparing the simulation results with published experimental data, we further apply the simulation technique to calculating the dielectric properties of hydrate-bearing sediments with varying porosities, hydrate saturations and micro-distributions in a broaden frequency. It is found that the dependence of dielectric behaviors on hydrate saturation is significantly affected by hydrate micro-distributions, especially in lower frequency range. We also find that relative permittivity at the frequency of 1 GHz decreases exponentially with increasing hydrate saturation, and the two coefficients that determine the exponential correlation are both dependent on the porosity of the sediments. Using the established relationships, the hydrate saturation in the Mount Elbert Well is successfully estimated from the measured dielectric data. The study provides a reliable tool for the accurate estimation of hydrate saturation in marine sediments.

Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): A case study in Krishna–Godavari basin (NGHP Exp-02)

The estimation of accurate reservoir parameters is essential for conventional and non-conventional hydrocarbon prospects. An artificial neural network has been developed to predict the reservoir parameters (porosity and saturation of gas hydrates) in a silty-sand, sandy-silt and pelagic-poor clay reservoir at two neighbour wells using the petrophysical information at another well in the Krishna–Godavari basin. The well log data were acquired during the Expedition-02 of Indian National Gas Hydrates Program (NGHP Exp-02). The estimation of gas hydrate saturation using Archie’s equation may be erroneous, as it is valid for the quantification of conventional hydrocarbons in the clean sand reservoir. Since the study area is clay dominated, it is subjective to adjust Archie’s exponents so that it matches with the saturation, measured from the core data. To overcome this problem of estimating the reservoir parameters in such a scenario, first of all we have derived porosity from the density log data and estimated saturation by employing modified Archie’s equation to the resistivity log data at one well. In order to train the network, the log data at one well are taken as inputs and corresponding porosity and saturation are taken as outputs. The reservoir parameters are then predicted at two neighbour wells using the wireline log data as input in those two wells. The predicted porosity and saturation of gas hydrates are alike to the traditionally estimated porosity and saturation at the neighbour wells. The predicted porosity in the studied region varies between 33 and 76%, whereas the saturation of gas hydrates ranges between 3.39 and 86.92%. This shows that the designed network can be used to estimate the reservoir parameters directly from the well log data in the same reservoirs.

Estimation of gas hydrate saturation at the slope in the northwest of South China Sea

Lack of drilling and inversion data is therefore difficult to calculate hydrate saturation in the region of interest. There remains no effective solution in the literature. This study combines the method of impedance index calculation and one-dimensional forward modeling to estimate the upper and lower limits of hydrate saturation. In the current work, the calculation of hydrate saturation includes three steps: (1) The relationship between hydrate saturation and P-wave impedance by equivalent medium theory is established and then is converted into the relationship between hydrate saturation and impedance index. (2) Impedance index of water-bearing and hydrate formation is obtained from the velocity model based on the forward modeling. (3) The upper and lower limits of hydrate saturation are obtained by plugging in the impedance index into the equation of hydrate saturation. This method is just apt for the situation with the range of porosity from 30% to 70%, velocity of matrix grains from 30% to 70%, and in-phase reflections. This study demonstrates that the upper and lower limits of hydrate saturation can be obtained in the region lack of drilling and inversion data.

Estimate of gas hydrate saturations in the Krishna-Godavari basin, eastern continental margin of India, results of expedition NGHP-02

Abstract A total of 25 drill sites were established in the Krishna-Godavari (KG) and Mahanadi Basins along the eastern margin of India during the India National Gas Hydrate Program Expedition 02 (NGHP-02) with the goal to identify and characterize gas hydrate occurrences in sand-rich reservoir systems. The objective of this study is to characterize the gas hydrate bearing zones linked with two seismic inferred horizons (R1 and R2) and estimate gas hydrate saturations at two sites in Area B of the KG Basin: Sites NGHP-02-22 and NGHP-02-23. The upper seismic-imaged reservoir section (reflector R1) contains both pore-filling and fracture-filling gas hydrate, while the lower seismic-imaged reservoir section (reflector R2) has only pore-filling gas hydrate. Since electrical resistivity seems to be the most pronounced physical response to the presence of gas hydrate, we have analyzed the resistivity log data using Archie's empirical equation from two sites based on an isotropic reservoir model. In order to assess the gas hydrate saturation, we have also performed isotropic velocity modelling using the three-phase Biot equation (TPBE) and density log derived sediment porosities. At Site NGHP-02-22, the average gas hydrate saturation estimated using the resistivity log data and P-wave velocity modelling are 29% and 17%, respectively in the depth interval of 207–290 mbsf whereas at Site NGHP-02-23, the average gas hydrate saturation obtained from resistivity log data and P-wave velocity modelling are 25% and 60%, respectively in the depth interval of 271–288 mbsf. Estimated results are further validated with the pressure core derived gas hydrate saturations to ensure their reliability at the respective drill sites. It was found that P-wave velocity log-inferred gas hydrates show a slight mismatch with the pressure core derived hydrate saturations while, the electrical resistivity log based result depicts a major mismatch with the pressure core derived hydrate saturations. The reason for this discrepancy is likely due to isotropic resistivity analysis of the reservoir section having anisotropic properties.

An approach to estimate gas hydrate saturation from 3-D heterogeneous resistivity model: A study from Krishna-Godavari basin, Eastern Indian offshore

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.

Ocean bottom seismometer data modeling to infer gas hydrate saturation in Krishna-Godavari (KG) basin

The velocities derived from seismic refraction data/well log/multi-channel seismic (MCS) data serve as the starting point for the saturation estimation of gas hydrates/free gas. Combined with well log data, the estimation of gas hydrate saturation by applying rock physics models to sonic P-wave velocity (Vp) can be greatly improved. The presence of gas hydrates usually changes the shear modulus of the sediment, and to make a reasonable quantification, both P-wave (Vp) and S-wave (Vs) velocities are to be considered, wherever available. The sonic S-wave velocity (Vs) data, if available, provides additional constraints in estimating saturation of gas hydrates. In 2010, CSIR-National Geophysical Research Institute acquired MCS and ocean bottom seismometer (OBS) station data in the Krishna-Godavari (KG) basin for the delineation of gas-hydrate reservoirs and quantifying the resource potential. This data is utilized in the present work, and the gas hydrate saturation is estimated using the seismically derived Vp and Vs in the effective medium theory (EMT). The Vp is determined using the MCS data and the hydrophone component (OBS-H) data of the OBS station that records the PP reflections, while Vs is determined from PS converted waves, recorded on the radial component (OBS-R) of the OBS station data. The results have delineated a five layer subsurface model with an average P-wave velocity of 1.8 km/s in the gas hydrate layer and 1.45 km/s in the free gas layer. The average S-wave velocity is of the order of 0.64 km/s in the gas hydrate layer and 0.5 km/s in the free gas layer. Gas hydrate saturation, computed by employing effective medium theory (EMT), shows that the average gas hydrate saturation varies from 16% to 18% of pore space along the profile.