Seismic Acoustic Impedance Inversion Research Articles

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

Seismic acoustic impedance inversion plays an important role in understanding subsurface structures and obtaining subsurface properties. The stochastic approach is one of the methods used for impedance inversion, and it aims to produce more reliable results by accounting for modeling uncertainty. Stochastic inversion represents the uncertainty of a subsurface model as a probability distribution and uses this distribution to estimate model parameters. In this study, seismic acoustic impedance inversion was performed using a Markov-chain Monte Carlo approach, which is a sampling method used in the stochastic process. Utilizing Bayesian inference based on prior information and observed data, we implemented acceptance probabilities and performed acoustic impedance inversion for field data through iterative calculations. We employed a single GPU card to execute the inversion algorithm and conducted a comparative analysis with the results obtained using a cluster composed of multiple CPU cores. Through a computational speed analysis, the efficiency of the algorithm using a GPU was verified, while uncertainty analysis was employed for algorithm validation. Through these analyses, we confirmed the feasibility of applying our developed GPU algorithm to tasks that require the inversion of extensive data, such as high-resolution seismic exploration and CO2 storage monitoring.

Dual Wasserstein generative adversarial network condition: A generative adversarial network-based acoustic impedance inversion method

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 Acoustic Impedance Inversion via Optimization-Inspired Semisupervised Deep Learning

Seismic acoustic impedance inversion (SAII) aims at recovering the subsurface impedance to achieve lithology interpretation. However, its ill-posedness and nonlinearity pose a great challenge to find an optimal solution. Regularization is an effective method to solve SAII by imposing prior information, but it suffers from high computational complexity and limited inversion performance. To mitigate the above limitations, we propose an optimization-inspired semisupervised deep learning SAII approach that incorporates the advantages between the model-driven optimization algorithm and the data-driven deep learning method. Specifically, it is implemented by parameterizing the alternating iterative method (AIM) by splitting it into two parts where the convolutional neural networks are adopted to learn the regularization terms and a nonlinear mapping and thus called the proposed network as AIM-SAIINet. The proposed method can not only simultaneously invert the seismic wavelet and impedance but also obtain high-resolution data as an intermediate product to facilitate the training of AIM-SAIINet and enhance the inversion accuracy. In addition, we introduce a joint semisupervised training scheme in which the network is first jointly pretrained in a supervised manner using the synthetic training data to provide good initial values, and then, a semisupervised training scheme is adopted to fine-tune it using few labeled data pairs to achieve high inversion accuracy. The synthetic and field data examples are conducted to validate the effectiveness of AIM-SAIINet, which achieves higher inversion accuracy at a fast computational speed compared with the traditional methods.

High-resolution seismic acoustic impedance inversion with the sparsity-based statistical model

Seismic acoustic impedance inversion plays an important role in subsurface quantitative interpretation. Due to the band-limited property of the seismic record and the discretization of the continuous elastic parameters with a limited sampling interval, the inverse problem suffers from serious ill-posedness. Various regularization methods are introduced into the seismic inversion to make the inversion results comply with the prespecified characteristics. However, conventional seismic inversion methods can only reflect fixed distribution characteristics and do not take into account discretization challenges. We have adopted a new poststack seismic impedance inversion method with upsampling and adaptive regularization. The adaptive regularization is constructed with two trained dictionaries from the true model and upsampled model-based inversion result to capture the features of high- and low-resolution details, and a sparsity-based statistical model is proposed to build the relationship between their sparse representations. The high-resolution components can be recovered based on the prediction model and low-resolution sparse representations, and the parameters of the statistical prediction model can be obtained effectively with conventional optimization algorithms. The synthetic and field data tests show that the model-based inversion is dependent on the sample interval, and our method can reveal more thin layers and enhance the extension of the strata compared with conventional inversion methods. Moreover, the inverted impedance variance of our method matches well with the borehole observations. The tests demonstrate that the interpolated model-based inversion result combined with the sparsity-based prediction model can effectively improve the resolution and accuracy of the inversion results.

Data-driven multichannel poststack seismic impedance inversion via patch-ordering regularization

Seismic acoustic impedance inversion plays an important role in reservoir prediction. However, single-trace inversion methods often suffer from spatial discontinuities and instability due to poor-quality seismic records with spatially variable signal-to-noise ratios or missing traces. The specified hyperparameters for seismic inversion cannot be suitable to all seismic traces and subsurface structures. In addition, conventional multichannel inversion imposes lateral continuity with a prespecified mathematical model. However, the inversion results constrained with specified lateral regularization are inferior when the subsurface situations violate the hypothesis. A data-driven multichannel acoustic impedance inversion method with patch-ordering regularization is introduced, in which the spatial correlation of seismic reflection is used. The method decomposes the seismic profile into patches and constructs the patch-ordering matrix based on the similarity among seismic patches to record the impedance structural extension. So the patch-ordering matrix can record the spatial extension of the acoustic impedance. Then, a simple regularization with difference operators of varying weights can reduce the random noise presented in the inverted impedance profile, stabilize the inversion result, and enhance the spatial continuity of the layer extension. The objective function for multichannel poststack seismic impedance inversion can be constructed by integrating the observed seismic record and the spatial continuity in the form of patch-ordering regularization, and it can be solved effectively with the limited-memory BFGS algorithm. The synthetic and field data tests illustrate the improvement of accuracy and lateral continuity of inverted results with our method, compared to conventional model-based inversion results.

A combination of genetic inversion and seismic frequency attributes to delineate reservoir targets in offshore northern Orange Basin, South Africa

Abstract The reported occurrence of Albian- and Cenomanian-aged braided fluvio-deltaic channels in the Orange Basin, South Africa, opens a window of exploration activities to characterize these channels as they are renowned to form some of the world’s giant oil field. In this study, a seismic acoustic impedance inversion and seismic attributes (instantaneous frequency and iso-frequency) analysis is used to investigate potential Albian and Cenomanian fluvio-deltaic channels in offshore, northern Orange Basin. Reservoirs were mapped using a well and 3D seismic volume (8-bit) after initial dip-steering coherency filtering had been performed on the seismic volume to remove incoherent noise and improve data resolution. Model-based acoustic impedance inversion was applied on the seismic volume to delineate fluvio-deltaic channels in addition to using the RMS (root mean square) amplitude attribute. Iso-frequency using the cosine correlative transform (CCT) method was equally applied to delineate these channels. Instantaneous frequency attribute was analyzed for potential hydrocarbon-charged sediments. This was achieved by utilizing thirty-three seismic traces as an input in the Hilbert transform window, after which trace envelope and instantaneous phase were transformed into instantaneous frequency. Acoustic impedance inversion results reveal the presence of two channels within the Cenomanian sequence, which shows high porosity (∼40%) along its geometry. The CCT method shows that the 8 Hz frequency window resolved the presence of a channel within the Albian sequence. A meandering channel within the Albian sequence was equally delineated by the RMS, while the application of instantaneous frequency (IF) attribute indicates the presence of hydrocarbon-charged sediments of Cenomanian age in proximity to a listric normal fault because of the attenuation of frequency observed close to the fault. This study demonstrates a case study of the application of seismic impedance inversion and seismic attributes for the delineation of potential reservoirs and hydrocarbon-charged sediments in a basin.

Integration of knowledge-based seismic inversion and sedimentological investigations for heterogeneous reservoir

Abstract Conventional geological modelling methods are not capable to provide precise and comprehensive model of the subsurface structures, when dealing with insufficient data. Knowledge based methods employing rule bases techniques are found vast applications in geoscience studies. These methods are applicable for petroleum reservoir geological modelling and characterizations, specifically for geologically complex structures. In this study, we present a knowledge based seismic acoustic impedance inversion method which employs rule based method for porosity estimation. The back propagation algorithm and the fuzzy neural network are also used in the methodology for parameter optimization and definition of nonlinear relationship between seismic attributes and porosity of the reservoir rock. The methodology initiates by seismic acoustic impedance inversion, followed by conventional porosity estimation. Subsequently, a knowledgebase was designed by investigation on more than 24 published case studies. This knowledgebase was used for definition of rules and optimization number of rules and improve efficiency of the inference engine. The porosity model obtained by conventional method in previous step would be used for primary evaluation of the rules. The extracted rules and optimized number rules then would be used for rule-based porosity estimation. The methodology was applied on a petroleum field containing two heterogeneous reservoir formations. Result of application of the proposed approach was evaluated with core analysis, thin sections and drilling data. Consistency of result obtained by the proposed method with geological data has shown its capability to resolve problem of insufficient data in reservoir geological modelling.

PERHITUNGAN CADANGAN HIDROKARBON FORMASI TALANG AKAR MENGGUNAKAN ANALISIS PETROFISIKA DAN SEISMIK INVERSI AI DENGAN PENDEKATAN MAP ALGEBRA PADA LAPANGAN BISMA, CEKUNGAN SUMATERA SELATAN

As a potential field in hydrocarbon prospect, Bisma field, the part of south Sumatra basin, can be evaluated in order to mapping the hydrocarbon accumulation and total reserve calculation purpose. Petrophysical analysis is an analytic method to evaluate the formation which sensitive with vertical contrast. Main output of this analysis is the compilation of some property value that useful on reservoir quality justification. Seismic acoustic impedance inversion is a method that can be used to define the distribution of porous zone as a hydrocarbon reservoir. This inversion result is the distribution of prospect area map by using combination of interpretation in AI map, density map and P-wave map. Map algebra is a calculation method that used to map that has the same grid number. By using those three methods, the reserve of hydrocarbon accumulation on Bisma field can be calculated. Petrophysical analysis results the indication of hydrocarbon in target zone is oil on two main layer, S and W3. Meanwhile, seismic inversion interpreting the distribution of porous zone is between 7400 – 9315 m/s*gr/cc in AI value context. Then, the effective porosity, Sw value and isopach are spread laterally using picked horizon and seismic acoustic impedance result as a guide, also, calculating the reserve. Layer S accumulating 21.1 million barrel oil and W3 accumulating 50.2 million barrel oil. This value resulted by aplicating Original Oil in Place (OOIP) equation on property map with map algebra approachment.

Integrating seismic acoustic impedance inversion and attributes for reservoir analysis over \u2018DJ\u2019 Field, Niger Delta

Structural interpretation and inversion analysis were used to characterize hard-to-image reservoirs, to predict subsurface interwell reservoir properties for optimum reservoir heterogeneity description, and to fine-tune drilling locations in ‘DJ’ Field, Niger Delta. Post-stacked 3D seismic data, composite well logs, and velocity checkshot data were used for the reservoir analysis. The study entailed mapping of structural framework, horizon picking, wavelet extraction, log editing and correlation, building of low-frequency model, acoustic impedance inversion and crossplot analysis of reservoir properties. Four major antithetic, three regional, and five minor faults were identified. The inversion results revealed an acoustic impedance range of 9700–25,000 ft/s g/cc and porosity range of 25–45% within the hydrocarbon-bearing sands. Crossplot analysis of Poisson and Vp/Vs ratio against acoustic impedance revealed Poisson ratio range of 0.30–0.45 and Vp/Vs ratio range of 1.3–2.50 within the delineated hydrocarbon-bearing sandstone interval. The overall correlation coefficient between the inverted and actual impedance was about 98% across the eight wells. Acoustic impedance slice at 2300 ms revealed low acoustic impedance sand within the range of 13,000–24,000 ft/s g/cc at the western and central part of the field. Comparing the acoustic impedance slice and seismic attribute maps at the target reservoir zones revealed high reflection amplitudes (bright Spots), indicative of hydrocarbon accumulation. The study predicts new and reliable drillable locations, by lithologic/fluid discrimination in the analysis of delineated reservoirs in the study area.

Bayesian acoustic impedance inversion with gamma distribution

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.

Introduction to special section: Seismic inversion — Conventional seismic impedance inversion and advanced seismic inversion techniques: Developments, workflow, and case studies

Seismic inversion is a key component of the quantitative seismic interpretation workflows for hydrocarbons exploration and production. In exploration with limited well control, seismic inversion for acoustic impedance helps to understand the spatial variations and identify potential “pay zones

Seismic acoustic impedance inversion with multi-parameter regularization

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 multi-trace 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 Wavelet Estimation Using Covariation Approach

This paper proposes a novel covariation approach for seismic wavelet estimation under the assumption that a real seismic signal follows non-Gaussian $\alpha$ -stable distributions. Since the non-Gaussian $\alpha$ -stable signals do not have finite second or higher order moments, the traditional methods of Gaussian distribution may not get a suitable solution. Based on the principle of fractional lower order statistics, the covariation approach deconvolution objective function matrix was given, and the details of wavelet estimation with the covariation approach were presented. Furthermore, computer simulation experiments on theoretical synthetic data and real seismic data were conducted. In the experiments, the effect of moments was considered. Among the estimated wavelets with different moments, the best wavelet should be the one with moment less than characteristic but close to characteristic. To verify the correctness and effectiveness of the proposed method, the extracted real wavelet was applied in real seismic acoustic impedance inversion. The result from the inversion of the 2-D real data set is consistent with the well log interpretation very well.

Acoustic impedance inversion with covariation approach

Abstract This paper presents a novel covariation approach for seismic inversion under the assumption that the real seismic signals follow non-Gaussian α-stable distribution. To verify the correctness and effectiveness of the proposed method, two computer inversion simulation experiments on synthetic data inversion and real seismic acoustic impedance inversion are conducted using covariation, covariance and Bayesian methods. In the inversion experiment, covariation method performances better than the covariance and Bayesian, and the inverse results are very close to the true solutions. In the real seismic data inversion case, the sample quantile method was applied to estimate the characteristic exponent of a seismic trace from a nearby well before inversion. The estimated characteristic was applied in the 2D seismic impedance inversion where the moment p of the whole survey is set to be less than it. The results from the inversion of the 2D real data set are consistent with the well log interpretation.

A Two-Dimensional Post-Stack Seismic Inversion for Acoustic Impedance of Gas and Hydrate Bearing Deep-Water Sediments Within the Continental Slope of the Ulleung Basin, East Sea, Korea

A post-stack inversion of 2D seismic data was conducted to estimate the spatial distribution of acoustic impedance associated with gas and hydrates in the Ulleung Basin, East Sea, Korea constrained by logs from three boreholes drilled on its continental margin. A model-based inversion was applied to a Plio-Quaternary succession composed of alternations of unconsolidated mass-flow deposits/turbidites. A comparison of seismic reflections and synthetic data computed from impedance logs is shown for two zones. An upper (steep) slope zone contains a moderately continuous, possibly bottom-simulating reflector feature along the corresponding section. This feature may be associated with a lithology boundary near a drill site in addition to, or instead of, a stability boundary of gas hydrates (i.e., gas below and hydrates above). The lower (gentle) slope zone has locally cross-cutting reflection patterns that are more likely to be attributed to gas- and hydrate-related physical phenomena than to spatiotemporal changes in lithology. This seismic inversion is informative and useful, making a contribution to enhance the interpretability of the seismic profiles for a potential hydrate recovery.

Seismic inversion for acoustic impedance and porosity of Cenozoic cool-water carbonates on the upper continental slope of the Great Australian Bight

Seismic inversion is used to estimate detailed seismic and rock properties, such as acoustic impedance and porosity, from seismic data. The method is widely used and proven successful by the petroleum industry, but has hitherto not been widely adopted for academic studies. This paper outlines a workflow and reports the application of model-based seismic inversion to a Cenozoic cool-water carbonate succession on the upper continental slope of the Great Australian Bight that was cored during Ocean Drilling Program Leg 182. Acoustic impedance data and the derived porosity distribution facilitate detailed studies of lithology, compaction and fluid flow in the shallow subsurface (0–500 m). A comparison of reflection and impedance data support the notion that seismic reflection events arise from bed boundaries rather than from lateral changes in impedance, even where these are significant. The uppermost continental slope of SW Australia is swept by a strong (>0.5 m/s) geostrophic current, the Leeuwin Current, and seismic profiles across the upper slope show geometrical similarities with contourite drifts. Cores taken through a conspicuous mounded seismic facies at ODP Site 1131 suggest that bryozoan build-ups nucleated on top of contourite mounds on the uppermost slope. Core recovery at three sites on a transect across the uppermost continental slope systematically decreased with increasing acoustic impedance and depth of the drilled section regardless of age. Because of the enhanced interpretability afforded by acoustic impedance and porosity data, and the possibility of predicting core recovery, the workflow outlined here should be of use in a broad spectrum of continental margin studies.

ABSTRACT: Seismic acoustic impedance inversion in reservoir characterization

ABSTRACT: Seismic acoustic impedance inversion in reservoir characterization

Acoustic impedance inversion pitfalls and some fuzzy analysis

This article describes some of the pitfalls which may be encountered when trying to integrate seismic and well log information through seismic acoustic impedance inversion. The article is illustrated using the well known Stratton 3-D seismic and well log data set, available from the Bureau of Economic Geology in Austin, Texas. The onshore Texas Stratton Field is comprised of stacked gas reservoirs and produces from Frio and Vicksburg reservoirs. The Frio unit is a sediment supply dominated fluvial/deltaic system characterized by rapid deposition and high subsidence rates. The Middle Frio, considered here, is relatively undeformed and flat‐lying with a gentle north‐to‐south elongated subsurface domal closure.

Construction of Reservoir Flow Models Using Deterministic and Stochastic Methods and Driven by Seismic Acoustic Impedance -- a Gulf of Mexico Example: ABSTRACT

Rapid and accurate subsurface descriptions and flow predictions contribute to profitable field development, particularly for deep water fields of modest size. This paper shows how 3-D seismic acoustic impedance played an essential roll in that process. This Tertiary field example exhibits a classic Gulf of Mexico {open_quotes}bright{close_quotes} seismic response. The slope channel deposits yielded reflections from stacked pay zones that were often interfering vertically and laterally variable. Seismic acoustic impedance inversion for volume estimation, well placement and flow model construction. Flow model construction was facilitated using Stratamodel, where reservoir boundaries, porosity and permeability were estimated from seismic acoustic impedance. This provided a {open_quotes}deterministic{close_quotes} flow model with which well choices and development economics were explored. Alternate flow models were developed in which the effect of fine scale (sub-seismic) heterogeneities were investigated. A 3-D {open_quotes}stochastic{close_quotes} model was developed that honored geostatistical parameters as well as seismic acoustic impedance. This gave insight to permeability distributions and confirmed that connectivity between scattered sand bodies would not significantly degrade the field performance predicted by deterministic models.