Inversion by pre‐migration spectral shaping

Most acoustic impedance sparse inversion methods include two parts: reflectivity inversion and extraction of acoustic impedance from reflectivity. This being the case, the acoustic impedance is easily affected by reflectivity and the value of acoustic impedance at time t=0, thus may become inaccurate. In this paper, we propose a seismic acoustic impedance inversion method with multi-parameter regularization. In our method, the acoustic impedance is calculated directly from objective functions to ensure the stability and accuracy of results. In addition, the total variation regularization, L1 norm constraint, and initial model constraint are used to make the inversion results better accordant with a priori geological information. Moreover, we use multitrace inversion to reflect the spatial and temporal correlation of the data. Since the objective function contains several different types of regularization term, we have developed an algorithm based on split Bregman iteration to calculate it. The inversion results of theoretical model and field data show that the proposed method has high inversion precision, strong anti-noise ability and good performance on stratigraphy delineation.

Seismic inversion in geophysics is a method that uses certain prior information, such as known geological laws and well logging and drilling data, to infer the physical parameters of underground media, such as wave impedance, velocity, and density, from seismic observation data, and thereby obtain the spatial structure and physical properties of underground strata. Seismic inversion is a highly complex problem with multiple solutions, and with the advancement of collection equipment, the volume of geophysical observation data is increasing at an astonishing rate. This presents new challenges for the accuracy and speed of seismic data inversion methods. There is an urgent need to develop intelligent and efficient inversion technologies for seismic inversion.Deep learning networks have powerful nonlinear fitting capabilities and can be used to solve complex nonlinear problems, such as seismic inversion. However, the predictive ability of deep learning networks largely depends on the quantity of training data. In the early stages of oil and gas exploration and development, the amount of well logging label data available for training is very limited, which poses a challenge for the application of deep learning in seismic inversion. Semi-supervised learning seismic inversion methods consider both data mismatch issues and well logging data mismatch issues, and can better adapt to inversion problems in real-world scenarios. Unlike supervised learning approaches, semi-supervised learning does not require a large amount of labeled data, thus it can better handle situations of data scarcity or mismatch.This paper utilizes a semi-supervised learning workflow to perform inversion on post-stack seismic data and has conducted experimental validation on the Marmousi 2 model. The experimental results show that, compared to supervised learning networks, the semi-supervised learning network still exhibits good predictive performance with a limited amount of data, demonstrating better stability in the presence of noise and geological variations, and effectively learns the mapping relationship between seismic data and artificial intelligence. Furthermore, as the amount of training data increases, the performance of the network also improves, confirming the importance of data quantity for training deep learning networks. The application results of the network on actual data indicate that the network has broad application prospects and feasibility. However, since the network is based on a channel-by-channel inversion method, there is still a lack of representation in terms of lateral continuity, which requires further exploration and improvement in subsequent research.

In the current age of declining oil prices, mature fields matter today more than ever. About 70% of the current world oil production is from mature fields. To unlock the remaining potential from these fields, new wells need to be implemented by utilising technological advancements in reservoir characterisation, well engineering and reservoir engineering. This paper focuses on improving reservoir understanding by using rock physics and seismic inversion technology on a mature field, which is located in offshore Sarawak Malaysia. Seismic data can be incorporated into an integrated workflow of predicting rock properties by utilising inversion processes that transform seismic data into a quantitative rock property, a descriptive measure of the reservoir. In this paper, an integrated workflow is adopted and it involves the application of rock physics driven seismic inversion for acoustic impedance (AI) prediction and geobody extraction. The extracted geobodies describe the areal extent of oil reservoir and changes in the rock property. The field being studied consists of multi-stacked channelised reservoirs containing substantial amount of crevasse splay sands. The main producing units are thin oil sands with initial gas caps. These reservoirs typically display a high degree of vertical and lateral heterogeneity. Seismic inversion and geobody prediction were used as a method for the prediction of petrophysical properties including porosity, Net-to-Gross (NTG), water saturation and permeability at geo-cellular scale. To this end, the information from different fault blocks was integrated for the simulation study and incorporated into the evaluation of the oil potential in this mature field. A holistic approach that comprises both a material balance study and a simulation study was adopted. The material balance study was used to verify the drive mechanism and to evaluate the transmissivity between different fault blocks. Prolific reservoirs were identified on inverted AI data and integrated into the static model for volumetric estimations and further high grading of the development drilling locations. As seismic inversion offers non-unique solutions, the integration of well log data allowed a technically acceptable answer as well as an estimation of the associated uncertainty. It can be concluded that rock physics driven seismic inversion results reduces uncertainty in property estimation during reservoir modelling while simultaneously improving field development plans. This paper focuses on how the integration of seismic and well data was used to optimally characterise an oil reservoir in a mature field development and create a fit-for- purpose reservoir model. The goal was to optimise reservoir characterisation and production management which leads to success and low risk re-development. Ultimately, the economics were improved through reducing the uncertainties in monetising the remaining by-passed oil reserves to a manageable range.

Stochastic seismic acoustic impedance inversion via a Markov-chain Monte Carlo method using a single GPU card

Prestack depth-migrated seismic data, having more accurate imaging position and amplitude fidelity than prestack time-migrated seismic data, are supposed to produce a higher quality reservoir prediction result by using depth-domain inversion. Some researchers have developed different methods of depth-domain seismic inversion. However, it has not been widely used in the industry probably because of two reasons: (1) it is a complex process to conduct depth-domain seismic inversion due to the nonstationary depth-domain seismic wavelet; and (2) time-domain seismic inversion is considered capable of solving the problem with less cost, both in regard to time and the economy. In this paper, we try to use the seismic waveform indicator inversion method in the depth domain. First, a forward model was built to demonstrate that seismic waveforms both in the time domain and the depth domain are highly correlated with lithologic associations. Second, a quantitative evaluation method of seismic data for reservoir prediction was proposed, which can help geophysicists estimate time-domain and depth-domain inversion effects before inversion. Finally, the seismic waveform indicator inversion method was implemented for presalt thin carbonate reservoir prediction in the Central Block at the eastern margin of the Pre-Caspian Basin. The depth-domain inversion result shows a relatively true structure and higher resolution validated by wells.

Bayesian model is popular in seismic acoustic impedance inversion because of the statistical information from Bayesian model. However, there are many distributions to simulate impedance distribution. Different distribution has different result. In this paper, the Gamma distribution is used as the prior distribution. A Bayesian impedance inversion method is proposed, under the assumption that the prior distribution is a Gamma distribution. Through the test from the field and model data, this method is feasible and this model based on Gamma distribution has a result with tall resolution.

Accurate seismic velocity inversion is crucial for oil and gas exploration. The popular fixed-scale regular-grid adjoint-state first-arrival (or first-arrival travel-time) slope tomography seismic velocity inversion method (FFAST) (or adjoint-state first-arrival slope tomography seismic velocity inversion method with fixed-scale regular-grid) can obtain good seismic velocity inversion results, but it still faces great challenges in achieving desirable high-precision seismic velocity inversion results due to its fixed-scale regular-grid. In this article, we use the multiscale staggered grid to replace the fixed-scale regular-grid in FFAST for model parametrization and propose the multiscale staggered-grid adjoint-state first-arrival (or first-arrival travel-time) slope tomography seismic velocity inversion method (MFAST), thereby obtaining high-precision seismic velocity inversion result. The staggered-grid is composed of a finite set of fixed-scale regular-grids with spatially staggered (or overlapped) relationships, which aims to change the grid coordinate to fully sample the structure information in the velocity model space with multiple fixed-scale regular-grids. The multiscale staggered-grid is composed of multiple staggered-grids with different fixed scales, which aims to adapt to the different scale complex structures in the velocity model space; in which, the large-scale staggered-grid based MFAST aims to reconstruct the large-scale background structures, thereby providing the essential guidance (or prior) information for the small-scale staggered-grid based MFAST which aims to obtain the detailed structural information. The model parametrization with multiscale staggered-grid is achieved by performing the model parametrization with regular-grid multiple times; the mean or median value of the outputs of multiple model parametrizations with regular-grid is considered the output of MFAST in the current iteration, and used to iteratively update the velocity model obtained by MFAST in the previous iteration. The checkboard and Marmousi model testing validate the effectiveness of MFAST.

Recent wireless communications systems have adopted orthogonal frequency-division multiplexing (OFDM) due to its robustness against fading channels with simple receiver structure. However, it results in poor power amplifier (PA) efficiency or otherwise suffers from severe nonlinear distortion that causes adjacent channel interference (ACI) because of its high peak-to-average power ratio (PAPR) property of transmit signals. To mitigate this issue, DFT-precoded OFDM has been adopted in the LTE-A standard, which virtually forms single-carrier signals and thus generates lower PAPR than the original OFDM. Nevertheless, as the information rate increases for better spectral efficiency, the performance of DFT-precoded OFDM with simple linear detection becomes inferior to that of the original OFDM over frequency-selective fading channels when both the systems are protected by high-rate channel coding. In this work, we propose an adaptive use of DFT-precoding for OFDM where the DFT operation is applied only when the generated OFDM symbol has large dynamic range and thus causes severe nonlinear in-band distortion after clipping and filtering. It is shown that a good trade-off in terms of bit error rate (BER) performance can be achieved while maintaining the specified level of the PA efficiency and the amount of out-of-band radiation.

Application of the least-squares inversion method: Fourier series versus waveform inversion

Deep learning neural networks offer some advantages over conventional methods in acoustic impedance inversion. Because labeled data may be difficult to obtain in realistic field data settings, it can be difficult to obtain high-accuracy inversion results. Some generative adversarial network (GAN)-based acoustic impedance inversion methods have been proposed to solve this problem. However, due to the existence of lateral discontinuity in inversion results of these GAN-based methods, inversion accuracy of these proposals is still not fully satisfactory. Therefore, to tackle the shortcomings of GAN, we develop an acoustic impedance inversion method based on dual Wasserstein GAN condition (dual-WGAN-c). Dual-WGAN-c can perform seismic inversion and forward calculation simultaneously and take the low-frequency information of acoustic impedance as conditional input to obtain accurate acoustic impedance inversion results in the case of insufficient labeled data. Through a data simulation experiment, compared with GAN-based seismic acoustic impedance inversion methods, dual-WGAN-c can obtain more accurate inversion results, with high robustness when the seismic data are noisy. Dual-WGAN-c also can obtain accurate results in acoustic impedance inversion of the recorded seismic data when one has insufficient labeled well-log data.

Seismic inversion methods are routinely used to estimate attributes such as P-impedance, S-impedance, density, P-wave and S-wave velocity, and elastic impedances from seismic and well log data. These attributes help to understand subsurface lithology and fluid content. There are several types of seismic inversion methods available in which model-based inversion has got more attention. In the present study, the model-based inversion and geostatistical methods namely probabilistic neural network (PNN), multi-layer feed-forward neural network (MLFN) and radial basis function neural network (RBFNN) are implemented to estimate acoustic impedance, porosity, density, gamma-ray volume and P-wave velocity in the inter-well region of the F-3 block, Netherlands. The aim of the study is to predict a number of petrophysical parameters and demonstrate how they can be used to interpret seismic reflection data. A comparative study is also performed to estimate the best suitable method to predict various petrophysical parameters. The assessment of inverted results demonstrates that the impedance in the region varies from 2550 m/s*g/cc to 6150 m/s/*g/cc, which is relatively low and indicates the presence of loose formation in the area. The correlation between synthetic seismic trace and initial seismic trace is estimated to be 0.98, and the synthetic relative error as 0.19, indicating excellent algorithm output. Further, the assessment of the geostatistical methods is carried out to predict petrophysical parameters in conjunction with Multi-Attribute Transform Analysis. The analysis shows that among the geostatistical methods, the PNN provides slightly better results. The petrophysical parameters that are estimated in this study are porosity, density, gamma-ray, and P-wave velocity volumes. These parameters reinforce the interpretation of seismic data, which is a significant step in any project of exploration and production. The technique is first implemented to the composite traces near to the well location, and the findings are analysed with well log data. Second, the entire seismic section is inverted for porosity, density, gamma-ray, and P-wave velocity after achieving satisfactory outcomes in the first step. The assessment demonstrates very high-resolution subsurface petrophysical parameters which strengthen the seismic data interpretation.

Post-stack seismic inversion utilizes post-stack seismic data along with well log data to estimate acoustic impedance. Post-stack seismic inversion is very fast compared to other pre-stack seismic inversion methods and provides a high-resolution subsurface image. This chapter discusses several types of post-stack seismic inversion methods namely model-based inversion, colored inversion, sparse spike inversion, and band-limited inversion. The chapter also includes the synthetic as well as real data examples of above seismic inversion methods.

This paper provides an overview of several seismic inversion approaches and their application to very- and ultra-high-resolution marine seismic datasets. The importance of seismic inversion is related to the current demands of the offshore engineering industry for estimates of elastic stress-related parameters of near-surface and bottom sediments. To meet these demands, the seismic inversion methods which facilitate the estimation of elastic properties of the subsurface should be developed for very high-resolution data. Several problems prevent the successful application of seismic inversion to high-resolution datasets. Firstly, borehole measurements, which are routinely applied to constrain seismic inversion, are often not available for the offshore near-surface. Secondly, very- and ultra-high-resolution seismic data are acquired in high frequency ranges, and low-frequency information is absent in such datasets. The elaboration of seismic inversion methods for ultra-high-resolution data is important for both practical and scientific purposes. In the paper, we share our experience in applying acoustic poststack inversion and prestack AVA inversion to very- and ultra-high-resolution marine seismic datasets.

Summary Numerical reservoir models are critical inputs for optimal decision-making and to increase exploration chances of success. Seismic inversion allows the prediction of petro-elastic subsurface properties from seismic reflection data and is an essential component in reservoir modeling and characterization. Traditional seismic inversion methods usually come with a high computational cost or suffer from issues concerning the non-linearity and the strong non-uniqueness of the seismic inversion model. With the generalization of machine learning in geophysics, deep learning methods have been proposed as efficient seismic inversion methods. However, most of them lack a probabilistic approach to deal with the uncertainties inherent to the seismic inversion problems and/or rely on complete and representative training data, which is often scarcely available. To overcome these limitations, we introduce a physics-driven deep learning methodology for predicting high-resolution subsurface models from pre-stack seismic data. The effectiveness of our approach is demonstrated on a three-dimensional real case from a Brazilian post-salt area. Our method produces improvements over the baselines and achieves promising performance on a real application example according to quantitative and qualitative evaluation metrics.

Seismic inverse modeling method based on generative adversarial networks