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.

De-risking Appraisal Phase in the Recent Gas Discovery in Banggai-Sula Basin, Central Sulawesi: Application of AVO Analysis and Pre-stack Inversion in the Sub-ophiolite Reef Carbonate Play

Following the success in the exploration drilling campaign in the last few years, Pertamina EP puts the recently discovered Wol Structure into the appraisal stage. The exploration wells Wol-001 and Wol-002 were spudded in 2017 and 2019 respectively, and both flowed a significant gas rate from an excellent reservoir of Miocene Reef of Minahaki Formation. A good understanding of the reservoir distribution was essential in such a stage. Therefore, a proper reservoir characterization was then carried out for further appraisal purposes. Using the improved quality data from the latest 5D interpolation-PSDM as input, integration of amplitude versus offset (AVO) techniques and rock physics analysis was conducted to investigate the hydrocarbon extent. The AVO class IIp was observed at the boundary between overlying Kintom Shale and gas saturated Minahaki limestone. It is indicated by a positive intercept (Ro), decreased amplitudes with offsets, and negative amplitudes in the far offsets. This polarity reversal characteristic is clearly seen from both AVO modeling and actual CDP in the well locations. Several CDPs inside and outside the closure were also examined to check the consistency. The slice of partial stack volumes has also exhibited a similar trend within the closure where class IIp is suggestive. Since the AVO attributes such as intercept and gradient solely were not able to visualize the reservoir extent properly, the pre-stack seismic inversion was performed to obtain a more accurate reservoir distribution through quantitative interpretation. A cross plot of P-impedance (Ip) over S-impedance (Is) differentiates the gas zone clearly from the wet linear trend. A depth slice at GWC (gas water contact) level describes that most of the Wol Structure is gas-saturated including the newly identified closure in the northwest. It is a three-way dip closure formed by limestone that was dragged upward by a thrust fault. Interestingly, it has a similar AVO response to the main Wol Structure which suggests a gas-bearing reservoir. This work brings an added value to the use of AVO analysis and pre-stack inversion for hydrocarbon mapping for appraisal purposes. Not only it has largely reduced the subsurface uncertainty, but also revealed an upside potential that is worth considering in future exploration.

New AVO Attributes and Their Applications for Facies and Hydrocarbon Prediction: A Case Study from the Northern Malay Basin

Amplitude versus offset (AVO) analysis integration to well log analysis is considered one of the advanced techniques to improve the understanding of facies and fluid analysis. Generating AVO attributes are one solution to give an accurate result in facies and fluid characterization. This study is focused on a field of Northern Malay basin, which is associated with a fluvial-deltaic environment, where this system has high heterogeneity, whether it is vertically or horizontally. This research is aimed to demonstrate an application of the scale of quality factor of P-wave (SQp) and the scale of quality factor of S-wave (SQs) AVO attributes for facies and fluid types separation in field scale. These methods are supposed to be more sensitive to predict the hydrocarbons and give less ambiguity. SQp and SQs are the new AVO attributes, which derived from AVO analysis and created according to the intercept product (A) and gradient (B). These new attributes have also been compared to the common method, which is the Scaled Poisson’s Ratio attribute. By comparing with the Scaled Poisson’s Ratio attribute, SQp and SQs attributes are more accurate in determining facies and hydrocarbon. SQp and SQs AVO attributes are integrated with well log data and considered as the best technique to determine facies and fluid distribution. They are interpreted by using angle-stack seismic data based on amplitude contrast on interfaces. Well log data, e.g., density and sonic logs, are used to generate synthetic seismogram and well tie requirements. The volume of shale, volume of coal, porosity, and water saturation logs are used to identify facies and fluid in well log scale. This analysis includes AVO gradient analysis and AVO cross plot to identify the fluid class. Gassmann’s fluid substitution modeling is also generated in the well logs and AVO synthetics for in situ, pure brine, and pure gas cases. The application of the SQp and SQs attributes successfully interpreted facies and fluids distributions in the Northern Malay Basin.

An integrated petrophysical-based wedge modeling and thin bed AVO analysis for improved reservoir characterization of Zhujiang Formation, Huizhou sub-basin, China: A case study

The main reservoir in Huizhou sub-basin is Zhujiang Formation of early Miocene age. The petrophysical analysis shows that the Zhujiang Formation contains thin carbonate intervals, which have good hydrocarbon potential. However, the accurate interpretation of thin carbonate intervals is always challenging as conventional seismic interpretation techniques do not provide much success in such cases. In this study, well logs, three-layer forward amplitude versus offset (AVO) model and the wedge model are integrated to analyze the effect of tuning thickness on AVO responses. It is observed that zones having a thickness greater than or equal to 15 m can be delineated with seismic data having a dominant frequency of more than 45 Hz. The results are also successfully verified by analyzing AVO attributes, i.e., intercept and gradient. The study will be helpful to enhance the characterization of thin reservoir intervals and minimize the risk of exploration in the Huizhou sub-basin, China.

Frequency-dependent AVO analysis using the scattering response of a layered reservoir

Sensitivity of reservoir properties to broadband seismic amplitudes can be weak, which makes interpretation ambiguous. Examples of challenging interpretation scenarios include distinguishing blocky reservoirs from fining sequences, low gas saturation from high gas saturation, and variable reservoir quality. Some of these rock and fluid changes might indicate stronger sensitivity to amplitudes over narrow frequency bands, which is a characteristic of frequency-dependent amplitude variation with offset (FAVO). We have developed a FAVO model for reservoir characterization, following a seismic scattering phenomenon through a set of isotropic elastic layers. The frequency dependency in our model comes from the time delays due to wave propagation within layers. The FAVO modeled response is a complex-valued amplitude varying with angle and frequency, and it is a function of the seismic velocities and thicknesses of individual layers, along with the conventional AVO response at all interfaces. Our FAVO seismic analysis consists of two main steps: (1) forward modeling using well logs to understand rock and fluid sensitivity to amplitudes to identify tuning frequencies with maximum amplitude excursions and (2) seismic analysis at tuning frequencies. With well-log models, we observed that the frequency-dependent tuning response is primarily dependent on the lithology stacking pattern of a reservoir; in the cases studied, the fluid and reservoir quality have secondary effects on the frequency dependence of the amplitudes. We evaluate synthetic models and field data from the Columbus Basin, Trinidad, to illustrate our frequency-dependent seismic analysis methods. For one of the sandstone reservoirs, a frequency-dependent attribute indicates better spatial resolution of the anomaly than a conventional amplitude extraction. FAVO attributes are complementary to conventional AVO attributes.