Post stack inversion of seismic data based on Semi-supervised learning

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.

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.

The transformation of seismic reflection data into quantitative rock properties can be very valuable in all phases of oil and gas exploration and production. In this study, public domain data from Australian offshore have been used to explain the logical workflow, which consists of systematic steps from seismic petrophysics to rock physics modeling and seismic inversion for quantitative rock property estimation. The application of seismic inversion technology offers many rewards, such as: better reservoir definition and management; better resource estimation; and reductions in uncertainty. The main benefit is that it improves direct and intangible interpretation of seismic data to provide meaningful geological boundaries in the subsurface. In order to provide the reliable estimate of rock properties for reservoir modeling the workflow is tested rigorously and divided into three categories which are based on the basic inversion types. The results reveal that relative impedance inversion should be performed first, which then followed by revision of seismic interpretation on impedance data. Full bandwidth rock properties are estimated by deterministic inversion. The results can be improved by several iterations of well to seismic tie, wavelet estimation, and low frequency models. As resolution with accuracy has always been challenging, the stochastic inversion approach is trailered for reservoir characterization and high resolution rock properties have been predicted. This produces a number of possible rock property models, as well as litho facies models and the results can be optionally constrained by the well data. It can be concluded that, to some degree, stochastic inversion is able to overcome the limited seismic bandwidth by integrating the rock physics properties of different lithologies. The examples of seismic inversion are discussed to demonstrate the significant benefits can be obtained by following an optimized workflow that is tailored to deal with the uncertainties that impact the end product. Introduction Seismic surveys are routinely performed to gather information about the geology of hydrocarbon fields. Seismic data, even when it has relatively low resolutions, can still deliver a structural model for the reservoir. The availability and coverage area of the data provides valuable information to constrain the reservoir model. Geophysicists are now able to extract greater quantitative information about reservoir rocks and fluid parameters from seismic data by working backwards using seismic inversion methods. Described as the flip-side of forward modeling, seismic inversion is the process of characterizing rocks and fluids that have produced the seismic data. In a sense, the process involves trying to determine the input (rock properties) by looking at the output (seismic data). It starts with actual measured data, removes the wavelet and derives acoustic impedance from reflection coefficient (Figure-1) with a low frequency model to infer rock properties and deliver earth model.

Seismic reflection amplitude data are increasingly used in reservoir modeling to provide information on changes in earth properties away from well locations. In geostatistical reservoir modeling, the most common application is to use seismic data as background data in some form of comodeling. Seismic data image reflectors and not earth layer properties. Therefore, prior to use in comodeling, seismic data must first be transformed into an earth layer property. Typically, the transform is to acoustic impedance using an appropriate seismic inversion method. Seismic inversion methods generate results that are generally band limited in nature, resulting in limits to vertical resolution. The vertical resolution achieved can be an order of magnitude below the vertical model resolution required from geostatistical reservoir modeling, which is in the order of well-log resolution. Hence, in using seismic data, geostatistical modelers encounter a problem of downscaling, not the more commonly encountered upscaling problem. This difference in scale introduces scatter between the primary data with well-log order resolution and the secondary seismically derived rock property data used in the comodeling. As a result, to preserve vertical heterogeneity, only limited use of the secondary data can be made in comodeling procedures. This results in models that only partially fit the seismic data, i.e., only limited use is made of the seismic information. If the secondary data are more strongly imposed, the fit to the seismic data improves, but the required vertical heterogeneity is not preserved. The inability to overcome this difference in scale issue, therefore, limits the value of the application of comodeling methods to integrate seismic data into reservoir models. One class of geostatistical methods that overcomes this limitation relies on iterative geostatistical modeling. In these methods, referred to as geostatistical seismic inversion, the iterative modeling process is conditioned such that the final models generated closely match the seismic data while maintaining the required vertical heterogeneity. The application of these methods is computationally expensive relative to comodeling methods but is now practical for large models on today's desktop hardware. Relative to comodeling, geostatistical seismic inversion methods make full use of the information carried in the seismic data, resulting in a significant reduction in model uncertainty away from well control.

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

Estimation of gas hydrate saturation in the Ulleung basin using seismic attributes and a neural network

Summary The seismic data spectral inversion method, arisen in recent years, can improve the resolution of seismic data for obtaining reservoir characteristics. It does not rely on prior information such as well-logging data. By using partial spectrum information from the input seismic trace data, a sparse reflectivity series can be inverted. The inverted reflectivity series can be used to improve the resolution and impedance inversion for thin layer reservoir prediction. In this paper, we implement seismic spectral inversion based on a compressed sensing algorithm, which can solve the ill-posed problem of the inversion process. In the process of inversion, we introduce a quadratic spectrum method to calculate the spectrum of seismic wavelet in the frequency domain, which can stabilize the inversion and handle real field data. Synthetic and real field seismic data examples show that the proposed spectral inversion based on compressed sensing algorithm is stable and effective. Moreover, the real field data inversion results show a good signal-to-noise ratio (SNR) and lateral continuity.

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.

Time-lapse (4D) seismic inversion is the leading method to quantitatively monitor fluid-flow dynamics in the subsurface, with applications ranging from enhanced oil recovery to subsurface CO2 storage. The process of inverting 4D seismic data for reservoir properties is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data and inaccuracies in the repeatability of 4D acquisition surveys. Consequently, ad-hoc regularization strategies are essential for the 4D seismic inverse problem to obtain geologically meaningful subsurface models and associated 4D changes. Motivated by recent advances in the field of convex optimization, we propose a joint inversion-segmentation algorithm for 4D seismic inversion that integrates total variation and segmentation priors as a way to counteract missing frequencies and present noise in 4D seismic data. The proposed inversion framework is designed for poststack seismic data and applied to a pair of seismic volumes from the open Sleipner 4D seismic data set. Our method has three main advantages over state-of-the-art least-squares inversion methods. First, it produces high-resolution baseline and monitor acoustic models. Second, it mitigates nonrepeatable noise and better highlights real 4D changes by leveraging similarities between multiple data. Finally, it provides a volumetric classification of the acoustic impedance 4D difference model (4D changes) based on user-defined classes (i.e., percentages of speedup or slowdown in the subsurface). Such advantages may enable more robust stratigraphic/structural and quantitative 4D seismic interpretation and provide more accurate inputs for dynamic reservoir simulations. Alongside presenting our novel inversion method, we introduce a streamlined data preprocessing sequence for the 4D Sleipner poststack seismic data set that includes time-shift estimation and well-to-seismic tie. Finally, we provide insights into the open-source framework for large-scale optimization that we used to implement the proposed algorithm in an efficient and scalable manner.

The bioclastic limestone reservoirs of Cretaceous period occupy an important position in the petroleum industry of Middle East. It is the carbonate heterogeneity that is challenging the accuracy of the reservoir prediction, which brings forward higher requirements for the seismic data quality. Besides, some seismic data are processed more than 10 years ago, the signal to noise ratio (SNR) is relative low due to the random noise and coherent noise like acquisition footprint anomalies. The acquisition footprint artifacts caused by acquisition and processing seriously suppress the true stratigraphic features, which can result in pitfalls in seismic interpretation, seismic attribute analysis as well as seismic inversion. While the pre-stack seismic data is usually unavailable, which means that the noise can hardly be subtracted by conventional pre-stack seismic processing workflows, such as statics, high-resolution velocity analysis and ground roll attenuation. Consequently, a comprehensive post-stack seismic data conditioning workflow is necessary to solve the above problems. In order to improve the post-stack seismic data quality, a comprehensive data conditioning workflow are applied for noise suppression. Firstly, structural-oriented filtering is utilized to attenuate random noise and partial acquisition footprint artifacts. Then 2D waveform transform of seismic amplitude and filtered seismic attribute in x-y domain are calculated, to separate acquisition footprint anomalies (large wave number in kx-ky domain) from true structural signal (small wave number in kx-ky domain) by interactive analysis. The application of Laplace-Gaussian (LoG) filter deserves an obvious improvement in acquisition footprint suppression workflow. The comprehensive noise attenuation workflow in this paper can effectively remove both periodic and non-periodic noise to obtain higher signal to noise ratio (SNR) for post-stack seismic volume. In this way, the stratigraphic features (tidal-channel, reef-beach complex) can be more clearly depicted and some artifacts caused by noise will disappear in seismic attribute calculation, seismic inversion and reservoir prediction.

To improve the accuracy of carbonate lithofacies modeling, mainly well data such as core, thin section and well logging data had been adopted in conventional methods. Although the reservoir types classification is very detailed, but it is usually difficult to integrate with seismic data to make 3D lithofacies model. To address the issue of carbonate lithofacies modeling, a new integrated carbonate lithofacies modeling technique was summarized based on thin section, core, well logging, 3D seismic data and production performance data. The integrated carbonate lithofacies modeling workflow mainly contains 5 steps. 1) Integrated lithofacies classification based on the core, thin section, well logging, FMI, CMR and production performance data. 2) Petrophysics lithofacies classification based on the cross-plot analysis between sensitive well log curves. 3) Petrophysics lithofacies prediction based on the sensitive well log curve by deep learning method, and verification by core lithofacies analysis. 4) Seismic inversion volume optimization by well lithofacies calibration. 5) Lithofacies modelling based seismic inversion based on the seismic inversion cut-off analysis (Fig.2). This workflow integrated seismic impedance (continuous variable) with lithofacies (discrete variable), and converts seismic inversion into lithofacies directly. According to the certification of new wells, this technique had been applied successfully in carbonate reservoir of M oil field in Middle East, it not only improves the accuracy of 1D lithofacies prediction for wells by deep learning method, but also improves the accuracy of 3D lithofacies modeling for the whole oilfield by well and seismic inversion integrated. The lithofacies modeling not only matched with lithofacies from core analysis and petrophysics lithofacies prediction from well log analysis, but also matched with seismic inversion data in no well area. The integrated carbonate lithofacies modeling workflow integrated thin section, core, well logging, 3D seismic data and production performance data, and improved the improves the accuracy of 3D lithofacies modeling for no well area. It’s useful for new wells optimization and high efficiency development with lower cost. The integrated carbonate lithofacies modeling workflow not only suit for carbonate reservoir, but also suit for clastic reservoir.

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

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 inversion is a key technique in reservoir characterization, enabling the transformation of seismic reflection data into quantitative rock property models such as acoustic impedance. This process is essential for enhancing gas field productivity by identifying reservoir features and estimating critical rock parameters. However, conventional inversion methods often struggle to accurately predict complex subsurface properties like porosity and density due to limitations in seismic resolution and the nonlinear nature of rock physics. To overcome these challenges, this study introduces an integrated methodology that combines acoustic impedance-based seismic inversion with Neural Network-driven multi-attribute analysis to evaluate a gas reservoir in the M Field, East Java Basin. Utilizing high-resolution pre-stack seismic data, the approach estimates the spatial distribution of porosity, density, and velocity. Additionally, the Neural Network analysis is employed to assess a shallower, less-explored interval, characterized by acoustic impedance values between 2948.46 and 3208.68 (g/cc*m/s), porosity levels of 18-20%, sandstone-like density ranging from 1.80 to 1.83 (g/cm 3 ), and Vp/Vs ratios from 1.83 to 1.77. This integrated workflow enhances reservoir prediction accuracy and deepens the understanding of gas-bearing zones in the study area.

Reducing the uncertainty of reservoir characterization requires to better identify the small‐scale structures of the subsurface from the available data. Studying the seismic response of meter‐scale, stratigraphic heterogeneities typically relies on the generation of reservoir models based on outcrop examples and their forward seismic modelling. To bridge geological information and seismic modelling, these methods allocate values of acoustic properties, such as mass‐density and P‐wave velocity, according to discretized properties like layer‐type lithology or facies units. This strategy matches the current workflow in seismic data inversion in industry, where modelling workflows are based on lithofacies distributions. However, from stratigraphic modelling, we know that meter‐scale heterogeneities occur within certain facies and lithologies. Here, we evaluate the difference on the seismic response between allocating acoustic properties in a grain size–based, semi‐continuous manner versus discretized manners based on lithology and facies classifications. To do so, we generate a reference geological simulation that we populate with acoustic properties, mass‐density and P‐wave velocity, using three different strategies: (1) based on grain size distribution; (2) based on facies distribution; and (3) based on lithology. The method we propose includes the generation of realistic geological simulations based on stratigraphic modelling and the transformation of its output into acoustic properties, honouring the intra‐lithology and intra‐facies, small‐scale structures. We, then, generate seismic data by applying a forward seismic modelling workflow. The synthetic data show that the grain size–based simulation allows the identification of small‐scale, stratigraphic heterogeneities, such as beds with strong density and velocity contrasts. These stratigraphic structures are smoothened or may completely disappear in the facies and lithology discretized simulations and, therefore, are not (well) represented in the synthetic seismic data. Recognizing meter‐scale, stratigraphic heterogeneities is relevant for the characterization of the fluid flow in the reservoir. However, current discrete and lithology‐based strategies in seismic inversion are not able to resolve such heterogeneities because real subsurface properties are not discrete properties but continuous, unless there are stratigraphic discontinuities such as erosional surfaces or faults. This research works towards a better understanding of the relationship between changes in these continuous properties and the observed seismic data by introducing greater complexity into the discretized geological simulations. Here, we use synthetic seismic images with the goal of eventually aiding in fine‐tuning seismic inversion methodologies applied to real seismic data. One pathway is to foster the development of inversion approaches that can leverage stratigraphic modelling to get stronger geological priors and replace the standard but inadequate multi‐Gaussian prior.