Exploration Geophysics - Volume 53, Issue 5, 2022

Full waveform inversion (FWI) makes full use of the seismic waveforms to find high-resolution velocity and density models by minimising residuals between the calculated and recorded data. Q attenuation widely exists in the subsurface media, leading to weak amplitude and misplacement of reflectors. However, the commonly used Q-compensated FWI (QFWI) based on the second-order wave equation has difficulties in simultaneously inverting velocity and density fields. A QFWI method based on new first-order viscoacoustic quasi-differential equations is proposed to simultaneously produce velocity and density fields. Based on the adjoint state inversion theory, Q-compensated forward-propagated operators, adjoint operators, and gradient equations are derived using the newly derived first-order viscoacoustic quasi-differential wave equations. The time-domain multi-scale decomposition method is introduced to update the velocity and density models from a low to a high wavenumber. Numerical examples on an actual work area model and a modified attenuating Marmousi model show that the proposed QFWI method produces higher-accuracy velocity and density models with iterations by correcting the Q attenuation than the conventional acoustic FWI. Even when the Q model is extremely inaccurate, the proposed QFWI obtains acceptable inversion results. Compared to the conventional QFWI, our QFWI better inverts velocity field in the case of an inaccurate density model. Finally, we verify the adaptability of our QFWI to field data.

The perfectly matched layer (PML) boundary condition has been widely used as a very effective absorbing boundary condition for seismic wavefield simulations. Convolutional PML (CPML) achieved by using a complex frequency-shifted stretch function was the latest development to further improve PML’s absorption performance for near-grazing angle incident waves as well as for low-frequency incident waves. However, the mathematical theory of the PML method is derived from the first-order equation, all PML implementations of second-order equations are to introduce auxiliary equations or variables to rewrite original PML equations, which will complicate the implementation. In this article, we propose a simple and efficient CPML implementation method for the second-order elastic wave equation, which directly simulates the second-order CPML equation. The main advantage of this method is that there is no need to introduce auxiliary variables or auxiliary equations to convert the second-order PML equation from the complex coordinate space to the real axis. Compared with the conventional CPML method for the second-order elastic wave equation, it introduces only eight convolution variables. We demonstrate the validity and absorption performance through extensive numerical experiments.

Petrophysical properties are the least explored parameters in the Mahanadi basin in the eastern Indian margin. We have used 2D seismic reflection data along a profile AC with available well logs of the Expedition-01 of the Indian National Gas Hydrates Program (NGHP) for the evaluation of lateral variation of porosity – a petrophysical property. The seismic line passes through the Sites NGHP-01-19B in the AB section, and NGHP-01-09A in the BC section. The porosity is estimated using two different approaches based on acoustic impedance (AI) transformation and direct seismic inversion. The AI transformation approach fails to decipher consistent porosity distributions whereas the direct seismic inversion approach provides satisfactory results along seismic line AC (AB and BC) in the Mahanadi Offshore region. The AI transformed porosity section illustrates erroneous porosity values within the target zone, irrespective of a strong coefficient of determination (R²) of 0.82 and 0.85 between AI and porosity at sites NGHP-01-19B and NGHP-01-9A, respectively. The accuracy of the porosity results obtained from the direct inversion approach depends on the best linear regression fit established between acoustic and porosity reflectivity-based models with a coefficient of determination as 0.91 at Site NGHP-01-19B and 0.94 at Site NGHP-01-9A. The porosity distributions are also corroborated with the density-porosity log values, and it is observed that porosity from direct seismic inversion is consistent with the porosity log information at respective sites. The porosity results at the two sites signify the overall good performance of the direct seismic inversion method and also indicate that the probability of free-gas occurrences in shallow sediments is higher compared to that of gas-hydrates.

Source-generated linear noise is one of the most common types of noise in seismic data. One or more groups of slanted linear events with strong energy often appear in seismic records and severely degrade the quality of subsurface reflections. Therefore, how to effectively remove this noise is the key to improve the quality of reflections. Here, we use a method that combines a feed-forward denoising convolutional neural network (DnCNN) with asymmetric convolution blocks (ACB) to attenuate linear noise in seismic data. Compared with traditional filter methods, this method involves less assumptions concerning the signals and noise; we merely train the neural network to recognise the features of reflections in seismic data. The DnCNN is a supervised deep learning method. It needs sufficient training data to optimise network parameters. Therefore, we generate numerous pairs thereof – including synthetic and real seismic data – to feed to the network. This input of data enables the network to identify reflections in seismic data directly and thus obtain the denoised data. Based on the characteristics of linear noise in seismic data, we build an asymmetric network architecture by combining the DnCNN with ACB. This enables the network to develop an ability to automatically identify reflected signals in seismic data. To validate the performance of the proposed method, we apply it to synthetic and real seismic data. The results demonstrate the method can effectively identify signals from noisy data and obtain better results in attenuation of linear noise and preservation of signals compared with the four other methods.

Single-physics rock templates have been widely addressed in the literature especially for sandstone reservoirs. Nevertheless, multi-physics rock templates (MPRT) have not been broadly studied to characterise complex carbonates. Multi-physics measurements lead to generate MPRTs which provide visual aid to petrophysicists and seismic rock physicists to classify facies and determine reservoir rock and fluids. Our research is oriented around two sequential stages. In the first stage, we make three independent porosity measurements (Archimedes, µCT and NMR) on core carbonate plugs from northern Niagaran reef. Resistivity, P&S-wave ultrasonic measurement and joint modelling of the same brine saturated plugs help us to fine-tune the model parameters through a global optimisation algorithm. Optimisation algorithm provides vuggy and micro-porosities close to independently measured porosities using NMR and µCT. In the second stage, we extend the technique from core data to well logs. We integrate mass balance equations to model bulk density and staged differential effective medium (SDEM) theory to model elastic and electrical resistivity of dual-porosity carbonates. Then, we design a stochastic global algorithm to simultaneously invert petrophysical properties. The inversion algorithm iteratively recovers the petrophysical properties including intergranular porosity, vuggy porosity, water saturation, salinity and matrix properties. Critical porosity, resistivity lithology exponents and sonic length scales for different pore systems are also estimated with meaningful accuracy.

The results of the geophysical surveys carried out in 2019 in the Abbey of Santa Maria di Cerrate (Lecce) are presented in this paper. The objective of this study was to achieve a better knowledge of the site, since it was only partially investigated by small archaeological excavation samples. The geophysical measurements were conducted inside and outside the church in order to identify buried ancient structures, using both the georadar and geoelectrical survey methodologies. The results of GPR (ground-penetrating radar), ERT (electrical resistivity tomography) and IP (induced polarisation) were jointly analysed with archaeological data already known and were georeferenced on the general plans of the site in order to obtain an overall view of the anomalies detected by geophysical instrumentation and probably linked to buried ancient structures.

Reservoir characterization and fluid discrimination based on seismic reflection amplitude play important roles in seismic exploration industry. Stable fluid-sensitive attributes from seismic data can help reduce uncertainties of hydrocarbon prediction in interwell locations and increase the reliability of drilling plans. In this study, in combination with seismic AVO inversion impedance and the rock physics template analysis, we proposed a new attribute (DI) to discriminate the hydrocarbon-associated anomalies and predict the reservoir parameters including porosity and water saturation quantitatively in Upper Gumai formation of W field, South Sumatra Basin. The new attribute (DI) is constructed using a set of combined impedances derived from prestack seismic inversion with the constraint of rock physics. Numerical modelling based on patchy-saturated model was implemented to test the sensitivity and stability of the new attribute and results showed that the DI attribute can predict the existence of hydrocarbon-filled sands with less ambiguity. With known well log data in W field, a feasibility study including cross-plotting and histogram methods was carried out and concluded that the DI attribute contributes higher resolution in distinguishing the hydrocarbon-filled sands from the background trend. In the process of quantitative interpretation, the DI attribute shows a good regression relationship with porosity and water saturation properties within the hydrocarbon sands with the correlation coefficients reaching 92% and 85%, respectively. By using seismic AVO inversion impedance, combining the application of the DI attribute and Vp/Vs ratio for lithology and fluid contents discrimination was conducted to improve the reservoir prediction accuracy in the field. Application results show that low values of the DI attribute (<18,000 m/s.g/cc) was indicative of hydrocarbon-filled sands effectively while Vp/Vs ratio presented a higher resolution in separating wet sand, dry-sand from shale. By using the linear regression relationship derived from cross-plotting analysis with well data, we calculated the volumes of porosity and water saturation from DI attribute and successfully screened out the hydrocarbon accumulation distribution, which reveals the potential zones of exploration interest in South Sumatra Basin.