Construction and Analysis of Three-Dimensional Seismic Porosity Inversion Models

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.

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.

High Resolution, Simultaneous VSP and Land Seismic Acquisition

Summary We present a method that combines seismic stratigraphic inversion and stochastic modeling techniques to generate reliable fine-scale impedance models with associated uncertainties. The seismic stratigraphic inversion technique provides an optimal acoustic impedance model at seismic scale consistent with the seismic data, the low frequency (filtered) well data and geological information. Then stochastic modeling techniques allow the generation of alternative realizations of fine-scale impedance models consistent with the optimal seismic scale impedance model, conditioned to high frequency well data and taking into account the scale difference between both information. Realizations filtered at different frequencies (i.e. different scales) are analyzed in term of uncertainties. It shows the strong impact of high frequency components of well data on the variability of the realizations and the necessity of constraining the very low frequency component of the acoustic impedance inverted model very carefully.

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

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.

Ram Powell is a stratigraphically trapped sequence of turbidite reservoirs in 2,500-4,000 ft of water in the Eastern Gulf of Mexico. The discovery was made in 1985 and the field has been the subject of extensive geoscience and engineering studies. In spite of this work there is still significant uncertainty in Ram Powell reserves. The Ram Powell development plan is underway, with fabrication of a tension-leg platform and 60,000 b/d plus 200 MMcf/d facility. Drilling will include 7 high rate horizontal wells and 3 water injection wells. Reserves accessed in this development scheme are approximately 250 million barrels equivalent. Model-based seismic inversion (constrained by 12 appraisal penetrations, rock property data and seismic interpretation) was employed over the main reservoir interval (J, L and N sands) to create models that are consistent with 3D seismic data. These models were used in several simulation studies including element models to determine optimal well placement, and full-field models to predict overall reservoir performance. The results of the study both reduced and enabled management of reservoir volume uncertainty; they were critical for determining the number, horizontal length and placement of wells (vertically and areally). The results also provided the basis for a proposal to increase the facility capacity limits to 70,000 b/d plus 260 MMcf/d. Some significant learnings are (1) model-based inversion is now in a production mode that can be rapidly applied to reservoir problems, (2) many reservoir complexities are simply below seismic resolution and inversion is unable to add with certainty significantly more resolution and, (3) detailed reservoir models that reflect reservoir conditions and are easily manipulated are required for reservoir optimization. Introduction Purpose. The Ram Powell Field is currently under development with fabrication of a tension-leg platform and 60,000 b/d and 200 MMcf/d production facility. Development will include 7 high rate horizontal wells and 3 water injection wells with a single subsea well drilled much later. Production is scheduled to begin in August, 1997 and the expected reserves are 250 million barrels equivalent. The purposes of this paper are (1) to describe the process used to integrate 3D seismic, geological, and rock property data into a static model that was used for reservoir simulation, (2) to illustrate how the modeling was used to manage uncertainty, and (3) to illustrate the impact of this work on development planning and facility design. Setting. Ram Powell is a stratigraphically trapped sequence of turbidite oil and gas reservoirs in 2,500-4,000 feet of water in the eastern Gulf of Mexico. The discovery was made in 1985 and has had extensive geoscience studies performed since then by each of the Ram Powell joint venture partners. A fairly extensive set of data is available including three 3D seismic surveys, twelve wells (drilled from five surface locations), 420 feet of conventional core, one production test, in addition to the normal set of petrophysical logs, RFT's, and sidewall samples (Figures 1, 2 and 3). In spite of the extensive data set, uncertainty remains with respect to Ram Powell reserves. Uncertainty in reservoir limits, net thickness, effective permeability (vertical and horizontal), fluid properties, well placement, and well design all have an impact on reservoir performance prediction (rate and cumulatives). There are three reservoirs in the initial development phase (J, L, and N sands). P. 477

Summary Acoustic impedance (AI) is often inversely proportional to porosity. This has led to a seismic reservoir characterization technology where the AI from seismic inversion is used to predict porosity away from well locations. The porosity is typically predicted from the absolute seismic AI derived from a model based inversion (MBI). There is an inherent error in AI derived from MBI due to the accuracy of the low frequency model (LFM) provided to the inversion engine, introducing an additional error in the predicted porosity. Here we show an use of relative AI after seismic Coloured Inversion to predict porosity in a target zone. This is possible if the seismic data have good low frequency content and if the target zone is small and appropriate for the field development. Within a zone of interest the sensitivity to the effects of the LFM is minimum and therefore relative AI can be sufficient to predict porosity. An Eagle Ford Shale example from the South Texas is used in this study.

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.

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.

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.

Seismic formation pressure logs are derived from seismic data in this study. The main objective is to delineate the distribution of overpressured zones in the subsurface, and seismic data from Offshore Louisiana are used to illustrate the proposed approach. The seismic data processing consists of velocity modeling, wavelet processing, and seismic inversion. From the acoustic impedances produced by seismic inversion, seismic velocity and density "logs" are derived at every CDP location using a relationship between sonic velocities and acoustic impedances (AI). Seismic formation pressure logs are computed against depth, making use of the above seismic velocity and density logs. Formation pressure logs are calculated assuming that compressional velocity, mean density, and depth are proportional to formation pressure. These logs are constrained at every depth point by estimated matrix and fluid compressional velocities (Vmax and Vmin). Vmax and Vmin are derived using porosity and sonic well log information. Results include profiles of seismic velocity logs, seismic density logs, and seismic formation pressure logs for two intersecting seismic lines from Offshore Louisiana. One well is used to constrain the data processing. The seismic formation pressure sections delineate a large region of overpressured shales in the subsurface. INTRODUCTION An important parameter in hydrocarbon exploration and exploitation is formation (or pore) pressure. In particular, detection of abnormally high formation pressures, or "overpressured" zones, can provide valuable information for exploration and exploitation purposes. High formation pressures are common in the Gulf of Mexico. Overpressured sediments are generally caused by a sequence of events wherein water becomes. trapped by faults or nonpermeable barriers in sediments at depth where, otherwise, the water would have been forced out by normal increasing overburden pressure (Bilgeri and Ademeno, 982). Abnormal fluid pressure is also caused by release of water into the pore system during clay diagenesis (smectiteillite transformation), and other mechanisms as listed in Bruce, 1984. High formation pressures cause major changes in subsurface rock parameters. In overpressured shales (Which contain pressured water), seismic velocity is lower, density is lower, and porosity is higher than if the pressured water had been able to escape (Bilgeri and Ademeno, 1982). Detection of overpressured sediments can also contribute to the overall analysis of hydrocarbon potential of a basin. Abnormal pressures exert partial control on the type and quantity of hydrocarbons accumulated because pressure potential determines the direction of fluid flow, and overpressuring partly controls the geometry of growth faults and related folds in basins where shale structures are the dominant type formed (Bruce, 1984). In the area of hydrocarbon exploitation, knowledge of formation pressure distribution can aid in conducting safe and efficient drilling operations. In this study, several recent advances in velocity modeling, seismic wavelet deconvolution, and inversion are incorporated to more accurately derive velocity and density "logs" from seismic data in both time and depth domains. (In his paper, seismically derived "logs" refers to a derived section where each output trace, or "log", has been generated from one CDP in the original seismic section).

<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>

A fast Bayesian inversion method for 3D lithology and fluid prediction from prestack seismic data, and a corresponding feasibility analysis were developed and tested on a real data set. The objective of the inversion is to find the probabilities for different lithology-fluid classes from seismic data and geologic knowledge. The method combines stochastic rock physics relations between the elastic parameters and the different lithology-fluid classes with the results from a fast Bayesian seismic simultaneous inversion from seismic data to elastic parameters. A method for feasibility analysis predicts the expected modification of the prior probabilities to posterior probabilities for the different lithology-fluid classes. The feasibility analysis can be carried out before the seismic data are analyzed. Both the feasibility method and the seismic lithology-fluid probability inversion were applied to a prospect offshore Norway. The analysis improves the probability for gas sand from 0.1 to about 0.2–0.4 with seismic data.

Seismic data interpolation and denoising play vital roles in obtaining complete and clean data in seismic data processing. Seismic data usually misses along various spatial axes and always mix with random noise. In order to obtain complete and clean seismic data, reconstruction technology can interpolate missing data and attenuate random noise. Nonsubsampled contourlet transform is an effective transform to obtain multi-scale and multi-direction sparse domain data for compression sensing interpolation and denoising. However, conventional iterative shrinkage/thresholding cannot handle ill-posed and ill-conditioned equations for solving linear inverse problem. We present a two-step iterative log thresholding method to overcome ill-posed and ill-conditioned problems and improve the convergence rate and solution accuracy, which can interpolate and denoise seismic data simultaneously in the nonsubsampled contourlet transform framework. First, we use nonsubsampled contourlet transform to convert the seismic missing data with random noise to sparse domain. Then, we apply two-step iterative log thresholding algorithm to interpolate and denoise data in sparse domain. The result of each iteration is based on the results of the previous two iterations, which can accelerate convergence rate. In addition, log thresholding can further improve convergence rate and solution accuracy. Finally, we use inverse nonsubsampled contourlet transform to obtain the interpolated and denoised seismic data. The new method can reconstruct the irregularly missing data and attenuate random noise to obtain complete and clean seismic data with high accuracy, which is crucial for seismic imaging and inversion. We demonstrate the applicability and effectiveness of this simultaneous interpolation and denoising technique with successful applications to both synthetic and field data examples.