Seismic acoustic impedance inversion with multi-parameter regularization

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.

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

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.

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

Research with 2D seismic data and wells used for acoustic impedance (AI) seismic inversion analysis and seismic attribute analysis has been carried out in the "CLM" Field of the North West Java Basin. The purpose of this study is to obtain the distribution pattern of acoustic impedance in the carbonate reservoir and to determine the hydrocarbon prospect zone in the study area. The acoustic impedance distribution pattern is obtained by the AI inversion process carried out on 15 2D post-stack seismic lines and 2 wells. Meanwhile, the identification of hydrocarbon prospect zones is carried out by integrating 3 maps, namely acoustic impedance slice maps, RMS amplitude attribute slice maps and envelope attribute slice maps. Based on the results of the acoustic impedance seismic inversion, it was found that the target reservoir zone in the Upper Cibulakan Formation was carbonate that had high AI values with a range of 42500-52500 (ft/s)(g/cc). Sandstone has a moderate AI value with a range of 34000-42500 (ft/s)(g/cc) and shale has a low AI value with a range of 21000-34000 (ft/s)(g/cc) which has the potential to become a seal rock. Meanwhile, based on the results of the analysis of seismic attributes, the hydrocarbon prospect zone is located in the northeast and northwest of the ITSNU-2 well location in the "CLM" field in the research area

Seismic method is one of the most frequently applied geophysical methods in the process of oil and gas exploration. This research is conducted in Nias Waters, North Sumatra using one line 2D post-stack time migration seismic section and two wells data. Reservoir characterization is carried out to obtain physical parameters of rocks affected by fluid and rock lithology. Seismic inversion is used as a technique to create acoustic impedance distribution using seismic data as input and well data as control. As final product, multi-attribute analysis is applied to integrate of inversion results with seismic data to determine the lateral distribution of other parameters contained in well data. In this research, multi-attribute analysis is used to determine the distribution of NPHI as a validation of hydrocarbon source rocks. In that area, there is a gas hydrocarbon prospect in limestone lithology in depth around 1450 ms. Based on the results of sensitivity analysis, cross-plot between acoustic impedance and NPHI are sensitive in separating rock lithology, the target rock in the form of limestone has physical characteristics in the form of acoustic impedance values in the range of 20,000-49,000 ((ft/s)*(g/cc)) and NPHI values in the range of 5-35 %. While the results of the cross-plot between the acoustic impedance and resistivity are able to separate fluid-containing rocks with resistivity values in the range about 18-30 ohmm. The result of acoustic impedance inversion using the model based method shows the potential for hydrocarbons in the well FYR-1 with acoustic impedance in the range 21,469-22,881 ((ft/s)*(gr/cc)).

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.

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismic data. Artificial neural network have been used to solve nonlinear inverse problems in recent years. This research obtained an acoustic impedance profile by feeding seismic profile and background impedance into a well-trained self-attention U-Net. The U-Net got convergence by appropriate iteration, and the output predicted the impedance profiles in the test. To value the quality of predicted profiles from different perspectives, e.g., correlation, regression, and similarity, we used four kinds of indexes. At the same time, our results were predicted by conventional methods (e.g., deconvolution with recursive inversion, and TV regularization) and a 1D neural network was calculated in contrast. Self-attention U-Net showed to be robust to noise and does not require prior knowledge. Furthermore, spatial continuity is also better than deconvolution, regularization, and 1D deep learning methods in contrast. The U-Net in this paper is a type of full convolutional neural network, so there are no limits to the shape of the input. Based on this, a large impedance profile can be predicted by U-Net, which is trained by a patchy training dataset. In addition, this paper applied the proposed method to the field data obtained by the Ceduna survey without any label. The predictions prove that this well-trained network could be generalized from synthetic data to field data.

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.

Model-constrained and data-driven double-supervision acoustic impedance inversion

Hydrocarbons are the main energy source in the world, especially in Indonesia, this is what makes hydrocarbons a natural resource that has been extensively explored to determine the presence of hydrocarbons. The exploration used is the Geophysics method, namely the seismic method and the well logging method, both methods are processed to provide an overview of the subsurface. The data processing technique used is acoustic impedance inversion modeling which aims to determine the characteristics of the reservoir based on changes in the acoustic impedance value in each layer. In this study using model-based acoustic impedance inversion using an artificial neural network (ANN) algorithm and the results obtained in the inversion analysis obtained an error of 0.002, so that the model can be used on seismic trace data to produce an acoustic impedance model. modeling section with a value of less than 5000 which may mean that there are hydrocarbons in the research location. Keywords: Artificial Neural Network, Hydrocarbons, Acoustic Impedance, Inversion.

<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>

Summary Seismic inversion for acoustic impedance can be robustly and efficiently performed through the application of a spectral shaping filter combined with a -90 o phase rotation. We show that this process, first published by Lancaster and Whitcombe (2000), is rigorously accurate and mathematically equivalent to all other methods of acoustic inversion based on a onedimensional convolutional model. Yet, the one-dimensional convolutional model is inadequate for describing migrated traces, because it ignores the dip dependence of the wavelet after migration. Any seismic inversion method assuming such a model, will cause severe amplitude distortions when applied to migrated data, excessively amplifying dipping events, signal or noise. We describe here three methods to perform seismic inversion, that overcome the problem described above, n amely: (i) applying spectral shaping before migration; (ii) applying spectral shaping to demigrated data, that are then remigrated; (iii) applying a multi-dimensional spectral shaping operator after migration.

Estimating Vsand and reservoir properties from seismic attributes and acoustic impedance inversion: A case study from the Mansuri oilfield, SW Iran

Seismic impedance inversion is a geophysical technique that transforms seismic data into quantitative subsurface properties, primarily acoustic impedance. This process enables the identification of rock boundaries, hydrocarbon reservoirs, and lithological variations, thus supporting informed drilling decisions and reducing exploration risks. However, conventional inversion methods face limitations such as noise sensitivity, low resolution, and reduced effectiveness in geologically complex areas, often resulting in oversimplified subsurface models. This study addresses these challenges by employing deep learning approaches, specifically LeNet, AlexNet, and conventional CNN architectures, to improve seismic resolution and synthetic seismogram generation. The methodology involves preprocessing seismic and well-log data, calculating acoustic impedance and reflection coefficients, and applying Continuous Wavelet Transform (CWT) for feature extraction. The models are trained using synthetic seismograms and validated against real seismic data. Among the models evaluated, AlexNet demonstrates superior performance in seismic data reconstruction, achieving the lowest MSE (0.0031), RMSE (0.0557), and MAE (0.052), along with the highest R2 score (0.993). The proposed technique demonstrates superior predictive accuracy, refined subsurface characterization, and reduced geological risk, thereby establishing a robust benchmark for advanced geophysical data analysis.