Transforming Seismic Reflection Data Into Quantitative Rock Properties by Seismic Inversion

Seismic inversion has been widely practiced in the oil and gas industry because it generates broad bandwidth of impedance data which maximizes vertical resolution and minimizes tuning effects. The lack of accurate prediction of lithology and fluid content of subtle features identified in seismic data acquired over the Sandfish field, Niger Delta, Nigeria necessitated the use of seismic inversion. In this paper, simultaneous seismic inversion is adopted to integrate seismic and well data for quantitative interpretation and uncertainty assessment of the subsurface reservoirs in the Sandfish field. Three Sandfish (Sfn) wells with the required petrophysical logs, check-shot data, high quality 3D seismic data of five angle stacks (6–12°, 12–18°, 18–26°, 26–32°, and 32–42°) were used for the analysis. A feasibility study including cross-plots of petrophysical and elastic properties from well data was first carried out to establish rock property relationships in the interval of interest. Biot-Gassmann fluid substitution analysis was also used to reveal sensitivity of rock properties to pore-fill type. Low frequency (0–2 Hz) models were generated from interpolation of high-cut-filtered P-sonic, S-sonic, and density logs guided by interpreted seismic horizons. The low frequency models were used to broaden the spectrum to estimate elastic volumes. The five partial angle stacks were simultaneously inverted using Jason’s Rock-Trace® inversion software which iterated trial inversions until the model sufficiently matched the seismic data. The inverted P-impedance (ZP), Simpedance (ZS), and density (ρ) were used to derive Poisson’s ratio (σ), volume of sand (Vsand), lambda-rho (λρ), and mu-rho (µρ). The cross-plot of λρ with µρ from well data looks similar to that from inverted results. Sands and shales are discriminated on the basis of sands having low values of µρ. Hydrocarbon-bearing sands are differentiated from water-bearing sands and shales on the basis of having lowest values of λρ. The Biot-Gassmann fluid substitution analysis at reservoir N-01 reveals typical class III amplitude variation with angle (AVA) responses for low-impedance hydrocarbon sands. The lithology and fluid prediction maps extracted from Vsand and σ at the N-01 seismic horizon show variation in lithology and fluid types for the entire volume. The inversion products reveal heterogeneities in the reservoirs away from well control validated by a blind well test. Hence, the study shows that rock-property model from a simultaneous inversion is an effective predictive tool for lithology and fluid types which in turn can guide well placement and predict reservoir development in the field of study.

Over the last project six months, our project activities have concentrated on three areas: (1) performing a stochastic inversion of pattern 16 seismic data to deduce reservoir permeability, (2) development of the geochemical inversion strategy and implementation of associated software, and (3) completing the software implementation of TProGS and the geostatistical analysis that provides the information needed when using the software to produce realizations of the Midale reservoir. The report partially the following deliverables: D2: Model development: MCMC tool (synthetic fluid chemistry data); deliverable completed. D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product; deliverable requirements partially fulfilled. D5: Field-based single-pattern simulations work product; deliverable requirements partially fulfilled. When completed, our completed stochastic inversion tool will explicitly integrate reactive transport modeling, facies-based geostatistical methods, and a novel stochastic inversion technique to optimize agreement between observed and predicted storage performance. Such optimization will be accomplished through stepwise refinement of: (1) the reservoir model - principally its permeability magnitude and heterogeneity - and (2) geochemical parameters - primarily key mineral volume fractions and kinetic data. We anticipate that these refinements will facilitate significantly improved history matching and forward modeling of CO{sub 2} storage. Our tool uses the Markov Chain Monte Carlo (MCMC) methodology. Deliverable D1, previously submitted as a report titled ''Development of a Stochastic Inversion Tool To Optimize Agreement Between The Observed And Predicted Seismic Response To CO{sub 2} Injection/Migration in the Weyburn-Midale Project'' (Ramirez et al., 2009), described the stochastic inversion approach that will identify reservoir models that optimize agreement between the observed and predicted seismic response. The software that implements this approach has been completed, tested, and used to process seismic data from pattern 16. A previously submitted report titled ''Model verification: synthetic single pattern simulations using seismic reflection data'', Ramirez et al. 2010, partially fulfilled deliverable D3 by summarizing verification activities that evaluate the performance of the seismic software and its ability to recover reservoir model permeabilities using synthetic seismic reflection data. A future progress report will similarly describe summarizing verification activities of the geochemical inversion software, thereby completing deliverable D3. This document includes a chapter that shows and discusses permeability models produced by seismic inversion that used seismic data from pattern 16 in Phase 1A. It partially fulfills deliverable D5: Field-based single-pattern simulations work product. The D5 work product is supposed to summarize the results of applying NUFT/MCMC to refine the reservoir model and geochemical parameters by optimizing observation/prediction agreement for the seismic/geochemical response to CO{sub 2} injection/migration within a single pattern of Phase 1A/1B. A future progress report will show inversion results for the same pattern using geochemical data, thereby completing deliverable D5. This document also contains a chapter that fulfills deliverable D2: Model development: MCMC tool (synthetic fluid chemistry data). The chapter will summarize model development activities required to facilitate application of NUFT/MCMC to optimize agreement between the observed and predicted geochemical response to CO{sub 2} injection/migration. Lastly, this document also contains a chapter that partially fulfills deliverable D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product. This work product is supposed to summarize model development activities required for (1) application of TProGS to Weyburn, (2) use of TProGS within the MCMC tool, and (3) application of the MCMC tool to address field seismic and geochemical data. The chapter included here fulfills requirements 1 and 2. Requirement 3 will be addressed in a future progress report.

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>

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.

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.

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 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.

Accurate subsurface modeling and characterization require the prediction of facies and rock properties within the reservoir model. This is commonly achieved by inverting geophysical data, such as seismic reflection data, using a two-step approach either in the discrete or the continuous domain. We have adopted an iterative simultaneous method, namely, stochastic perturbation optimization, to invert seismic reflection data jointly for facies and rock properties. Facies first are simulated according to a Markov chain model, and then rock properties are generated with stochastic sequential simulation and cosimulation conditioned to each facies. Elastic and seismic data are computed by applying a rock-physics model to the realizations of petrophysical properties and a seismic convolutional model. The similarity between observed and synthetic seismic data is used to update the solution by perturbing facies and rock properties until convergence. Coupling the discrete and continuous domains ensures a consistent perturbation of the reservoir models throughout the iterations. We have evaluated the method in a 1D synthetic example for the estimation of facies and porosity from zero-offset seismic data assuming a linear rock-physics model to demonstrate the validity of the method. Then, we apply the method to a real 3D data set from the North Sea for the joint estimation of facies and petrophysical properties from prestack seismic data. The results show spatially consistent rock and fluid inverted models in which the predicted facies reproduce the vertical ordering as observed in the well data.

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.

The Cheleken fields, located in southern Caspian Sea, are made of a succession of stacked sand reservoirs over a 6km time window, with significant areal and vertical variations. A seismic inversion was carried out with the aim of improving the understanding and delineation of these complex reservoirs. 3D OBN seismic data, covering the area of interest, was recently acquired. A tailored processing and velocity model building workflow was designed to produce a good quality seismic data volume and a reliable velocity model which were used as inputs for a subsequent reservoir characterization study. Deterministic and stochastic inversions followed by facies and porosity estimation based on statistical methods were applied to predict reservoir occurrence and quality. Due to the large time window of the stacked sand reservoirs, the inversion required separate wavelets and facies/porosity model calibration per interval. An integrated process, including seismic data, well logs, seismic velocity fields as well as the interpreted horizons, was adopted to better characterize the targets and succeeded in predicting reliable rock properties away from well boreholes. The inverted elastic attributes were converted into sand/shale facies using a Bayesian classification approach. Despite the limited seismic resolution and a significant overlap in the elastic domain between facies, the method showed good predictability at the well locations and provided a better understanding of the spatial sand distribution. In addition, a new multi-attribute statistical transform approach applied for reservoir property estimation helped predicting the porosity to further optimize future well planning. One of the benefits of the employed statistical methods is the ability to gauge uncertainties associated to the estimates in the form of facies probability and porosity standard deviation. In particular, the facies probability proved valuable for detecting thin sands and mapping their spatial extension. The main challenge of the Cheleken fields is the presence of thin stacked sands that are difficult to interpret and to map on the seismic data. The statistical methods applied to interpret the inversion results allowed a better mapping of the thin sands across the volume. A new approach for reservoir property estimation based on a multi-attribute statistical transform was successfully tested to provide additional reservoir quality estimation.

Summary The main goal of reservoir characterization is the description of the subsurface rock properties (i.e. porosity, volume of minerals and fluid saturations). This is commonly done in a sequential, two-step, approach: elastic properties are inferred from seismic inversion, which are then used to compute rock properties by applying calibrated rock physics models. However, this sequential procedure may lead to biased predictions as the uncertainties may not be propagated through the entire process. To overcome these limitations, here we propose the inference of shale rock properties directly from seismic data using a geostatistical direct shale rock physics AVA inversion. The purpose of the proposed geostatistical direct shale rock physics AVA inversion is to extract the properties included in the composition of a shale volume, such as brittleness, TOC and porosity from the seismic reflection data. The proposed method is applied to a real dataset from a Lower Paleozoic shale reservoir in Northern Poland.

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.

In order to properly accomplish the reservoir characterization process is necessary integrating all available information about the field in a consistent model. It is not an easy task to perform this integration in practice, for which is necessary to use some specific methods such as seismic inversion. By means of seismic inversion is possible to integrate efficiently both well-log and seismic data, obtaining a model that could be used in a forecasting process. The seismic inversion can be achieved by several methods, which can be divided in two main groups: the deterministic ones and the stochastic methods. In this work we show how stochastic inversion can improve the reservoir characterization process, by comparing its results with those obtained by deterministic inversion. As a matter of fact, the stochastic inversion can use a high sample rate that is close to the cell size of reservoir models, implying that a more reliable model is generated. As additional benefits the stochastic method can generate some basic statistics measurements that improve interpretations and also, due the great number of realizations generated during the process, it is possible to perform an uncertainty study on the reservoir model.

Numerical 3D high-resolution models of subsurface petroelastic properties are key tools for exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas, the available data set is often composed exclusively of seismic reflection data due to the lack of drilled wells and are therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations, we have developed a geostatistical framework that allows inverting seismic reflection data without the need of experimental data (i.e., well-log data) within the inversion area. This iterative geostatistical seismic inversion methodology simultaneously integrates the available seismic reflection data and information from geologic analogs (nearby wells and/or analog fields) allowing retrieving acoustic impedance models. The model parameter space is perturbed by a stochastic sequential simulation methodology that handles the nonstationary probability distribution function. Convergence from iteration to iteration is ensured by a genetic algorithm driven by the trace-by-trace mismatch between real and synthetic seismic reflection data. The method was successfully applied to a frontier basin offshore southwest Europe, where no well has been drilled yet. Geologic information about the expected impedance distribution was retrieved from nearby wells and integrated within the inversion procedure. The resulting acoustic impedance models are geologically consistent with the available information and data, and the match between the inverted and the real seismic data ranges from 85% to 90% in some regions.