AVO analysis for high amplitude anomalies using 2D pre-stack seismic data

Amplitude variation with offset (AVO) analysis is an 1 efficient tool for hydrocarbon detection and identification of elastic rock properties and fluid types. It has been applied in the present study using reprocessed pre-stack 2D seismic data (1992, Caulerpa) from north-west of the Bonaparte Basin, Australia. The AVO response along the 2D pre-stack seismic data in the Laminaria High NW shelf of Australia was also investigated. Three hypotheses were suggested to investigate the AVO behaviour of the amplitude anomalies in which three different factors; fluid substitution, porosity and thickness (Wedge model) were tested. The AVO models with the synthetic gathers were analysed using log information to find which of these is the controlling parameter on the AVO analysis. AVO cross plots from the real pre-stack seismic data reveal AVO class IV (showing a negative intercept decreasing with offset). This result matches our modelled result of fluid substitution for the seismic synthetics. It is concluded that fluid substitution is the controlling parameter on the AVO analysis and therefore, the high amplitude anomaly on the seabed and the target horizon 9 is the result of changing the fluid content and the lithology along the target horizons. While changing the porosity has little effect on the amplitude variation with offset within the AVO cross plot. Finally, results from the wedge models show that a small change of thickness causes a change in the amplitude; however, this change in thickness gives a different AVO characteristic and a mismatch with the AVO result of the real 2D pre-stack seismic data. Therefore, a constant thin layer with changing fluids is more likely to be the cause of the high amplitude anomalies.

A successful qualitative and quantitative integrated interpretation of Lower Miocene wells and seismic data in Salina del Istmo Basin, Mexico

The study area is located in middepth to deep waters of the Salina del Istmo Basin where Repsol operates Block 29. The objective of this work is to integrate qualitative and quantitative interpretations of rock and seismic data to predict lithology and fluid of the Early Miocene prospects. The seismic expression of those prospects differs from age-equivalent well-studied analog fields in the U.S. Gulf of Mexico Basin due to the mineralogically complex composition of abundant extrusive volcanic material. Offset well data (i.e., core, logs, and cuttings) were used to discriminate lithology types and to quantify mineralogy. This analysis served as input for developing a new rock-physics framework and performing amplitude variation with offset (AVO) modeling. The results indicate that the combination of intercept and gradient makes it possible to discriminate hydrocarbon-filled (AVO class II and III) versus nonhydrocarbon-filled rocks (AVO class 0 and IV). Different lithologies within hydrocarbon-bearing reservoirs cannot be discriminated as the gradient remains negative for all rock types. However, AVO analysis allows discrimination of three different reservoir rock types in water-bearing cases (AVO class 0, I, and IV). These conclusions were obtained during studies conducted in 2018–2019 and were used in prospect evaluation to select drilling locations leading to two wildcat discoveries, the Polok and Chinwol prospects, drilled in Block 29 in 2020.

Microcrack Porosity Estimation Based on Rock Physics Templates: A Case Study in Sichuan Basin, China

Low porosity-permeability structures and microcracks, where gas is produced, are the main characteristics of tight sandstone gas reservoirs in the Sichuan Basin, China. In this work, an analysis of amplitude variation with offset (AVO) is performed. Based on the experimental and log data, sensitivity analysis is performed to sort out the rock physics attributes sensitive to microcrack and total porosities. The Biot–Rayleigh poroelasticity theory describes the complexity of the rock and yields the seismic properties, such as Poisson’s ratio and P-wave impedance, which are used to build rock-physics templates calibrated with ultrasonic data at varying effective pressures. The templates are then applied to seismic data of the Xujiahe formation to estimate the total and microcrack porosities, indicating that the results are consistent with actual gas production reports.

Demonstrating multiple attenuation with model-driven processing using neural networks

Some of the key tasks in seismic processing involve suppressing multiples and noise that interfere with primary events. Conventional multiple attenuation on seismic prestack data is time-consuming and subjective. As an alternative, we propose model-driven processing using a convolutional neural network trained on synthetically modeled training data. The crucial part of our approach is to generate appropriate training data. Here, we compute a generic data set with pairs of synthetic gathers with and without multiples. Because we generate the primaries first and then add multiples, we ensure that we have perfect target data without any multiple energy. To compute generic and realistic training data, we include elements of wave propagation physics and implement a randomized flexibility of settings such as the wavelet, frequency content, degree of random noise, and amplitude variation with offset effects with each gather pair. A fully convolutional neural network is trained on the synthetic data in order to learn to suppress the noise and multiples. Evaluations of the approach on benchmark data indicate that our trained network is faster than conventional multiple attenuation because it can be run efficiently on a modern GPU, and it has the potential to better preserve primary amplitudes. Multiple removal with model-driven processing is demonstrated on seismic field data, and the results are compared to conventional multiple attenuation using a commercial Radon algorithm. The model-driven approach performs well when applied to real common-depth point gathers, and it successfully removes multiples, even where the multiples interfere with the primary signals on the near offsets.

Pseudoelastic pure P-mode wave equation

We have developed a pseudoelastic wave equation describing pure pressure waves propagating in elastic media. The pure pressure-mode (P-mode) wave equation uses all of the elastic parameters (such as density and the P- and S-wave velocities). It produces the same amplitude variation with offset (AVO) effects as PP-reflections computed by the conventional elastic wave equation. Because the new wave equation is free of S-waves, it does not require finer grids for simulation. This leads to a significant computational speedup when the ratio of pressure to S-wave velocities is large. We test the performance of our method on a simple synthetic model with high-velocity contrasts. The amplitude admitted by the pseudoelastic pure P-mode wave equation is highly consistent with that associated with the conventional elastic wave equation over a large range of incidence angles. We further verify our method’s robustness and accuracy using a more complex and realistic 2D salt model from the SEG Advanced Modeling Program. The ideal AVO behavior and computational advantage make our wave equation a good candidate as a forward simulation engine for performing elastic full-waveform inversion, especially for marine streamer data sets.

The vertical image projection method for migration stretch compensation

True amplitude inversion is often carried out without taking into account migration distortions to the wavelet. Seismic migration leaves a dip-dependent effect on the wavelet that can cause significant inaccuracies in the inverted impedances obtained from conventional inversion approaches based on 1D vertical convolutional modelling. Neglecting this effect causes misleading inversion results and leakage of dipping noise and migration artifacts from higher frequency bands to the lower frequencies. I have observed that despite dip-dependency of this effect, low-dip and flat events may also suffer if they are contaminated with cross-cutting noise, steep migration artifacts, and smiles. In this paper I propose an efficient, effective and reversible data pre-conditioning approach that accounts for dip-dependency of the wavelet and is applied to migrated images prior to inversion. My proposed method consists of integrating data with respect to the total wavenumber followed by the differentiation with respect to the vertical wavenumber. This process is equivalent to applying a deterministic dip-consistent pre-conditioning that projects the data from the total wavenumber to the vertical wavenumber axis. This preconditioning can be applied to both pre- and post-stack data as well as to amplitude variation with offset (AVO) attributes such as intercept and gradient before inversion. The vertical image projection methodology that I propose here reduces the impact of migration artifacts such as cross-cutting noise and migration smiles and improves inverted impedances in both synthetic and real data examples. In particular I show that neglecting the proposed pre-conditioning leads to anomalously higher impedance values along the steeply dipping structures.

Amplitude variation with offset analysis of nonbright spots for gas channel identification: A case study from the western Sichuan Basin, China

The Shaximiao Formation in the Zhongjiang Gas Field of the Sichuan Basin was initially a high-productivity gas field with the bright spot channel as the vital exploration target. With further development, gas wells were obtained in some nonbright spot areas, which caused interpreters to pay great attention to the channels with nonbright spot abnormal amplitudes. We have developed a method to delineate nonbright spot channels from the complicated sand-mudstone contact relationship. First, we classified sandstone into types I, IIa, IIb, and III, depending on the responses of the amplitude variation with offset from the drilled data, to produce a forward model. We the explain why the hidden channel cannot be identified using the full-angle stack seismic data based on this model. Afterward, we put forward a difference, between the synthetic seismogram responses of bright and nonbright channels, in creating seismic-to-well ties for nonbright channels. This difference from bright channels is that the synthetic data’s wave peak is not corresponding to the peak of the real seismic data. The wave trough has the same situation. Finally, we used far-angle stack seismic data to calculate coherent energy and instantaneous spectral attributes (the latter produced for red-green-blue blending) to identify the hidden channel. We observed that parts of the channel are more clearly visible in the far-angle stack than in the full-angle stack data. In the latter situation, we cannot describe the geometric shape of the channel elaborately. The Shaximiao Formation example is a relatively effective analog for nonbright spot plays compared with elsewhere.

Basin-wide rock-physics analysis in Campeche Basin, Gulf of Mexico — Phase II: Reservoir rock and fluid properties

We present a basin-wide rock-physics analysis of reservoir rocks and fluid properties in Campeche Basin. Reservoir data from discovery wells are analyzed in terms of their relationship between P-wave velocity, density, porosity, clay content, Poisson's ratio (PR), and P-impedance (IP). The fluid properties are computed by using in-situ pressure, temperature, American Petroleum Institute gravity, gas-oil ratio, and volume of gas, oil, and water. Oil- and gas-saturated reservoir sands show strong PR anomalies compared to modeled water sand at equivalent depth. This suggests that PR anomalies can be used as a direct hydrocarbon indicator in the Tertiary sands in Campeche Basin. However, false PR anomalies due to residual gas or oil exist and compose about 30% of the total anomalies. The impact of fluid properties on IP and PR is calibrated using more than 30 discovery wells. These calibrated relationships between fluid properties and PR can be used to guide or constrain amplitude variation with offset inversion for better pore fluid discrimination.

Radon Transform Based on Waveform for AVO-Preserving Data Construction

The traditional hyperbolic Radon transform suffers from the major problem of how to both obtain a high resolution and preserve the amplitude variation with offset (AVO). In the Radon domain, high resolution (sparseness) is a valid criterion. However, if a sparse model is obtained in the Radon domain due to averaging along the offset direction, then it is not possible to preserve the AVO in the inversion data. In addition, hyperbolic Radon transform has a time-variant kernel based on a traditional iterative algorithm, the conjugate gradient (CG), which requires significant computation time. To solve these problems, we propose a Radon transform based on waveform that contains both cycle and amplitude characteristics of seismic waves. The new transform entails creating an upper envelope for the seismic data and computing a preliminary forward Radon transform in the time domain. The forward Radon transform incorporates a priori information by measuring the energy of each slowness (p) trace to obtain the high-resolution result of the Radon domain. For AVO preserving, the proposed method uses polynomials to describe the AVO characteristics in the inverse Radon transform based on the least-squares inversion. Besides amplitude preserving and high resolution, the proposed method avoids using CG and greatly reduces the cost of computing hyperbolic Radon transform in the time domain. In applications to both synthetic and field data, waveform Radon transform (WRT) has a better performance than the conjugate gradient Radon transform (CGRT).

Model misspecification and bias in the least-squares algorithm: Implications for linearized isotropic AVO

When inversions use incorrectly specified models, the estimated least-squares model parameters are biased. Their expected values are not the true underlying quantitative parameters being estimated. This means the least-squares model parameters cannot be compared to the equivalent values from forward modeling. In addition, the bias propagates into other quantities, such as elastic reflectivities in amplitude variation with offset (AVO) analysis. I give an outline of the framework to analyze bias, provided by the theory of omitted variable bias (OVB). I use OVB to calculate exactly the bias due to model misspecification in linearized isotropic two-term AVO. The resulting equations can be used to forward model unbiased AVO quantities, using the least-squares fit results, the weights given by OVB analysis, and the omitted variables. I show how uncertainty due to bias propagates into derived quantities, such as the χ-angle and elastic reflectivity expressions. The result can be used to build tables of unique relative rock property relationships for any AVO model, which replace the unbiased, forward-model results.