Extended poroelastic impedance

The need for WRFM (well, reservoir, and facility management) with infill wells cannot be over-emphasized and has become essential for assets maturation and ullage utilization. EEI is the application of angle rotation in the conventional acoustic impedance under certain approximation. It essentially works by projecting intercept and gradient together with different angles which highlight different features. Extended Elastic Impedance (EEI) has proved to be a highly convenient framework for seismic AVO studies. EEI logs can be directly related to the petrophysical properties of interest, and seismic EEI reflectivity volumes can be obtained directly from the prestack data via linear projection in sin2 θ. In this project, we modelled the "chi" angles from the logs and applied the different chi angles to the prestack seismic dataset. This can be likened to a bandlimited seismic inversion product. The chi angles of the lithology indicators- Share Impedance (SI), MuRho and Density and the fluid indicators- Saturation, Fluid factor (FF) and LamdaRho were derived and applied on the seismic dataset for generating a 3D property distribution map. The input to the EEI inversion is the seismic angle sub-stacks of near (0-10deg), mid (10-20deg) and far (20-30deg) solving the AVA_2term Shuey AVO fitting function- intercept and gradient only. In some cases where the seismic S/N ratio in the ultra-far angle stack is high, it could be considered for the inversion and would give rise to the 3rd term- curvature, but in our case, it was considered noisy. To ensure stability of the inversion, the substacks were aligned both spectrally and on the waveforms and the seismic dataset was further post-processed. Amplitude maps generation and sculpting were done on the different EEI generated seismic volumes and the result provided insight in the lithology and fluid study at the reservoirs of interest. This has led to field life extension by several years more than design life and is helping to keep xxxkbpd-sized facility full via world class WRFM, infill drilling and growth projects. The EEI result helped in identified new leads and revalidated existing leads aiding well placement.

Extended Elastic Impedance (EEI) inversion is a seismic inversion method developed for delineating fluid and lithology. EEI can estimate different elastic parameters, such as Shear Impedance, ratio, Bulk modulus, Lame parameters and Poisson's ratio. The most significant ability of EEI method is that it can directly relate impedance attribute to petrophysical parameters such as porosity, water saturation and clay volume which are critical for determining reservoir zone. Every single parameter can be evaluated by extending incident angle beyond that of in Aki and Richards’s equation and giving different weight to intercept and gradient values. This method is particularly useful in areas where the sandstone reservoir has similar impedance with surrounding shale.

The study aims to implement a high-resolution Extended Elastic Impedance (EEI) inversion to estimate the petrophysical properties (e.g., porosity, saturation and volume of shale) from seismic and well log data. The inversion resolves the pitfall of basic EEI inversion in inverting below-tuning seismic data. The resolution, dimensionality and absolute value of basic EEI inversion are improved by employing stochastic perturbation constrained by integrated energy spectra attribute in a Bayesian Markov Chain Monte Carlo framework. A general regression neural network (GRNN) is trained to learn and memorize the relationship between the stochastically perturbed EEI and the associated well petrophysical log data. The trained GRNN is then used to predict the petrophysical properties of any given stochastic processed EEI. The proposed inversion was successfully conducted to invert the volume of shale, porosity and water saturation of a 4.0 m thick gas sand reservoir in Sarawak Basin, Malaysia. The three petrophysical geobodies were successfully built using the discovery wells cut-off values, showing that the inverted petrophysical properties satisfactorily reconstruct the well petrophysical logs with sufficient resolution and an accurate absolute value at the well site and are laterally conformable with seismic data. Inversion provides reliable petrophysical properties prediction that potentially helps further reservoir development for the study field.

Extended Elastic Impedance (EEI) has proved to be a highly convenient framework for seismic AVO studies. EEI logs can be directly related to the petrophysical properties of interest and seismic EEI reflectivity volumes can be obtained directly from the prestack data via linear projection in sin2theta. The directness of the EEI method is one of its strengths, making it unnecessary to perform intermediate steps such as examining conventional AVO attributes (e.g., intercepts and gradients). Consequently it can be difficult for those more familiar with the conventional AVO analysis to understand the parallels between the two methods. Once these parallels are understood we show how they can be exploited to provide new AVO attributes for background Vs/Vp ratio and AVO class based on the EEI seismic projection method.

Summary Extended elastic impedance (EEI) is an extension of elastic impedance (EI) which is a generalization of acoustic impedance (AI) for nonzero angles of incidence and can be tuned to be proportional to reservoir properties. In this study, we estimated the P- (Vp) and S-wave velocities (Vs), P-wave to S-wave velocity ratio (Vp/Vs), and Poisson’s ratio of the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA, using the EEI inversion technique. Data used in the study include 3D prestack seismic data from the southern part of the field and four wells, selected from a large well database. Vs logs at the wells were constructed from Vp logs using the empirical relationships. The percent prediction errors for the four velocity properties are less than about 5% except for Poisson’s ratio at one well, supporting that the EEI inversion can be used in prediction of rock properties.

In the Abu Madi Formation of the Nile Delta Basin, false bright spots may be misinterpreted as being indicative of hydrocarbons due to mixed clastics and carbonates. However, rock-physics analysis of well logs in a particular prospect area where such ambiguity exists suggests that attributes derived using extended elastic impedance (EEI) inversion may help identify hydrocarbons because they better show anomalous behavior in particular directions that are readily related to pore fluids and lithology. The EEI attributes calculated from well logs correlate extremely well to lithology and fluid properties, thereby differentiating amplitude anomalies caused by gas-bearing sandstones encased in shale from similar amplitudes caused by juxtaposition of high-impedance carbonates over lower impedance water-filled sandstones. Comparing seismically derived EEI attributes to well logs from a productive well and a nonproductive well indicates that seismic inversion can successfully identify lithologies such as shales, sandstones, carbonates, and anhydrite and distinguish gas-bearing from water-bearing sandstones. The technique can thus potentially be used to better delineate and risk prospects in the area, as well as assisting exploration efforts in other locations where similar ambiguities in amplitude interpretation exist.

In this tutorial, I present an overview of the techniques that are in use for prestack seismic amplitude analysis, current and historical. I show that these techniques can be classified as being based on the computation and analysis of either some type of seismic reflection coefficient series or seismic impedance. Those techniques that are based on the seismic reflection coefficient series, or seismic reflectivity for short, are called amplitude variation with offset methods, and those that are based on the seismic impedance are referred to as prestack amplitude inversion methods. Seismic reflectivity methods include: near and far trace stacking, intercept versus gradient analysis, and the fluid factor analysis. Seismic impedance methods include: independent and simultaneous P and S-impedance inversion, lambda-mu-rho analysis, Poisson impedance inversion, elastic impedance, and extended elastic impedance inversion. The objective of this tutorial is thus to make sense of all of these methods and show how they are interrelated. The techniques will be illustrated using a 2D seismic example over a gas sand reservoir from Alberta. Although I will largely focus on isotropic methods, the last part of the tutorial will extend the analysis to anisotropic reservoirs.

Up till now the EI (Elastic Impedance) and EEI (Extended Elastic Impedance) approaches have been applied exclusively to CI (Coloured Inversion) data of pairs of angle stacks or an intercept and gradient stack. This paper introduces a simple re-casting of the EI and EEI methods that allows the procedure to be applied directly to calibrated model based pre-stack seismic inversion data. A key advantage of the approach is that it only requires Zp and Zs as inputs (density is not required) and can therefore be applied to the vast majority of pre-stack inversion data volumes that have already been created. Additionally log and elastic impedance values are directly comparable as the seismic data is in the impedance domain whereas CI volumes are still in the reflectivity domain.

Summary The utilization of Extended Elastic Impedance (EEI) proves to be a rapid and accessible method for gaining insights into lithology, fairway determination, and reservoir properties, with some indications towards hydrocarbon fluid assessment. However, seismic EEI encounters challenges in terms of uncertainty and reliability, primarily stemming from poor calibration to well data. In the well feasibility study stage, the EEI-transformed property, utilizing raw recorded logs as input, consistently exhibits a wide ranges of Chi angles for different wells. Consequently, the technology is often criticized as unreliable and deemed unsuitable for deployment. This study addresses the critical role of well data pre-conditioning as a prerequisite for successful EEI applications. Four recently acquired wells within close proximity, each equipped with complete elastic logs (density, p-sonic, and s-sonic), are examined in this study. The designated area features water depths ranging from 500 to 1600 meters, predominantly characterized by clastic sediment composed of clay and quartz minerals.

Compressional and shear wave travel times are used to determine rock dynamic elastic parameters. Elastic parameters are used in obtaining the in-situ stresses of the rock, calculate safe mud weight, avoid wellbore instability problems and design optimum hydraulic fracture geometry. Often overlooked, weakness in the calculation of elastic parameters studies is a major cause of well-bore problems and unsuccessful well stimulation jobs. In well logging, compressional and shear wave travel time logs are one of the common logs to be recoreded in almost every formation. In cases when the sonic logs are missing, these logs values are estimated using empirical correlations. So far, none of the available models or correlations are universally accepted by log analysts since every model estimates the log values significantly different from the others, which may be suitable for one reservoir and not for other. As a result, inaccurate values can potentially raise major concerns throughout the life of the well. The error in sonic log values affects the elastic parameters, which can result in either underestimation or overestimation of the in-situ stresses. So, the objective of this paper is to develop robust empirical model for compressional and shear wave travel times using different functional network approach. For this purpose, well logs data, such as; gamma ray, bulk density, and neutron porosity were collected from different wells which are drilled in a carbonate formation. Functional network tool is used to develop mathematical model for compressional and shear wave travel times. This AI model were trained on the data of several well and validated on the remaining wells. Different types of functional network tools were studied, namely; forward selection model FNFSM, backward elimination model FNBEM, forward backward model FNFBM and backward forward model FNBFM. Then based on correlation coefficient R and average absolute percentage error AAPE, a comparison was made and the one which performed better was selected for prediction of sonic wave transit times. The novelty of this work is that for the first time, the explicit mathematical model is developed from functional network to estimate compressional and shear wave travel time. The mathematical model developed from the functional network is easier to implement. The new model will eliminate the need to use expensive software to run AI model. The new model for sonic transit time significantly reduces the uncertainties in calculating the sonic transit times in carbonate formation.

Young’s modulus and Poisson ratio are essential parameters for rock brittleness evaluation. At present, prestack inversions of these parameters mostly adopt linear approximation, whereas the nonlinear inversion methods that are based on the exact Zoeppritz equation are seldomly conducted. Nevertheless, in the case of large incident angles and high-impedance contrast, the nonlinear equation has higher accuracy than the linear approximation. It is more in line with the nature of geophysical inversion problems, and it describes the variation of formations’ elastic parameters better than the linear approximation does. Therefore, based on the exact solution of the Zoeppritz equation, we have parameterized and derived a new nonlinear reflection coefficient equation using nonlinear Young’s modulus, Poisson ratio, and density (YPD) as variables. Subsequently, by combining the inverse operator algorithm with the YPD equation, we develop a new nonlinear amplitude-variation-with-offset inversion scheme. This approach can robustly extract Young’s modulus and Poisson ratio from seismic data and identify shale-gas sweet spot locations within the reservoir, as well as predict reservoir fluid properties. We have tested the accuracy and applicability of this approach with synthetic and real data examples.

This study presents a comprehensive workflow to detect low seismic amplitude gas fields in hydrocarbon exploration projects, focusing on the West Delta Deep Marine (WDDM) concession, offshore Egypt. The workflow integrates seismic spectral decomposition and machine learning algorithms to identify subtle anomalies, including low seismic amplitude gas sand and background amplitude water sand. Spectral decomposition helps delineate the fairway boundaries and structural features, while Amplitude Versus Offset (AVO) analysis is used to validate gas sand anomalies. The entire seismic volume is classified into facies domains using machine learning, which isolates target features from seismic background data. The study area, covering 1850 km2, includes major structures such as the Rosetta fault and Nile Delta offshore anticline, with reservoirs consisting of layered sandstones and mudstones. Over 90 wells, including exploration and development wells, have been drilled in the area. Seismic amplitude data, including full and partial offset stacked, were analyzed to classify gas, water, and shale zones. The workflow’s performance is demonstrated through the successful identification of the low-amplitude Swan-E Messinian anomaly, characterized as a high-risk gas prospect. Machine learning techniques, specifically neural network models, were trained to differentiate seismic features such as low-amplitude gas sand from background-amplitude water sand and shale. By iterating over multiple attributes and validating the models on blind test sets and on a blind section, which excluded a known shallow gas field, the workflow significantly improved the ability to detect potential hydrocarbon reservoirs characterized by low seismic amplitude. The results show that this integrated approach reduces exploration risk, quantifies the chance of success, and enhances decision-making in well placement and hydrocarbon exploration. This method is particularly useful for identifying low seismic amplitude anomalies, which are often challenging to detect with conventional seismic analysis. (1) This study developed a workflow to detect low seismic amplitude gas fields in near-field exploration. (2) It uses a machine learning algorithm to classify and explore low-seismic-amplitude gas sand reservoirs. (3) This approach helps estimate the likelihood of success and reduces the risk associated with hydrocarbon exploration wells.

Quantification of the parameters of twisting–rotating acupuncture manipulation using a needle force measurement system

A seismic example from the Malay Basin is presented, demonstrating improved seismic imaging beneath gas clouds using full-wavefield imaging approaches. Overall imaging concepts, synthetic examples, and field implementation strategies are discussed, and results that tie with well information are presented. Seismic imaging beneath gas clouds using the full-wavefield redatuming technique improves the image by estimating the waveform transmission operators via equivalent-medium representation of the overburden from the gas cloud reflection response for use in a form of multidimensional deconvolution of the wavefield. The other example shown uses the full-wavefield migration seismic imaging technique, which utilizes primaries and higher-order multiples as signals to improve the reflectivity estimation in imaging. The demonstrated full-wavefield imaging approach uses information carried by the gas cloud reflection response to correct seismic image distortion. Removing the internal multiple using conventional demultiple processing in the gas cloud area will also remove the valuable information of the subsurface that it carries. Such multiples must be preserved for this method to be successful. The information is translated into transmission operators that are estimated by simulating the reflection response through an effective medium of the gas cloud overburden. The effective medium is obtained via nonlinear full-waveform inversion techniques from the reflections of the gas cloud overburden area. Finally, a deconvolutional process removes the transmission operators from the gas cloud reflections and recovers the underlying reflectors. Full-wavefield imaging can reconstruct the amplitudes of the reflection response below a gas cloud overburden zone so that the complex transmission imprint on the area underneath is removed properly. The Malay Basin field case study shows that implementation of this approach can provide a reliable amplitude image of the subsurface affected by gas clouds, calibrated and verified by the well information.

Full-waveform inversion is widely used to reconstruct subsurface properties at different geologic scales. For shallow land applications using surface waves, a lack of information on the source wavelet, dispersion, and presence of higher modes increases the nonlinearity of the inverse problem. The inversion can become more challenging with the presence of near-surface complexities associated with scattering, attenuation, and high-contrast variations in the elastic parameters. Compared with the least-squares formulation, GSOT provides a more convex misfit function and reduces dependence on the accuracy of the initial model. Although a few field-data applications have shown the potential and benefits of using GSOT-based FWI with body waves, there are limited real applications of the inversion with a GSOT misfit function for NS characterization. Despite considerable effort with blind benchmark tests in exploration seismology, typically synthetic FWI examples for NS applications are demonstrated through an “inverse crime” approach. Synthetic FWI examples performed compare the performance of LS- and GSOT-based FWI with more realistic scenarios. We demonstrate the GSOT misfit function improves the initial 1D velocity models and guides the updates toward the actual subsurface properties. This enables the recovery of higher-mode Rayleigh waves and reconstruction of the cavity with better precision.