Seismic Driven Lithofacies Integration for Geological Model Infilling in a Deep Offshore Turbiditic Field Case Study

The development of effective algorithms for the simulation of facies in petroleum systems continues to be limited to techniques relying on linear spatial dependence; Truncated Gaussian Simulation, Plurigaussian Simulation and Sequential Indicator Simulation, in addition to pattern-based techniques based on Multiple Point Statistics. Recent research in the development of new geostatistical techniques for continuous variables has found that the requirement of linear spatial dependence is very rarely satisfied. Datasets from micrometre to kilometre scale have been analysed spatially and, in all cases, significant non-linearity in the spatial dependence structure have been observed. This paper replaces the Gaussian ‘engine’ in Truncated Gaussian Simulation and Plurigaussian Simulation, which requires linear spatial dependence, with a copula-based algorithm that is able to model non-linear dependence structures. The advantage is a facies model that does not contain the symmetric spatial artefacts that characterise Gaussian-based models. In flow simulation terms, this means that the connectivity (and directional connectivity) of different facies within the reservoir architecture to be modelled with greater fidelity, honouring the expected continuities of each of the facies elements. Furthermore, extending recent research by Horning and Bardossy on directional and order asymmetry for continuous variables to consider their application to facies models allows the development of facies-models that are able to represent the directional asymmetry found in sedimentary depositional systems. This allows a better representation of the distal and proximal elements in depositional systems as well as the opportunity to model complex asymmetries associated with meandering channel and estuaries.

Relay ramps may considerably control fluid flow across otherwise sealing fault zones and, therefore, they are subjected to fluid flow simulation studies in relation with groundwater extraction and petroleum production. However, these studies used mainly deterministic modelling approach based on field observations. In this paper, we extend this approach by incorporating a conditional simulation technique, i.e. truncated Gaussian simulation (TGS), to provide stochastic elements in the modeling work and to improve the representation of detailed relay ramp structure. TGS attributes (facies proportions and variogram ranges of the Gaussian field) are defined based on field observations of the Delicate Arches Ramp, Utah, which is characterized by the presence of three structural sets. Groups of facies defined based on the structural sets are modelled independently using TGS, and the final model is generated by merging the TGS realizations. The relay ramp facies model is used to generate upscaled models. The upscaling procedure use petrophysical values and coarse grid used in a recent flow simulation study of the Delicate Arches Ramp deterministic model. Flow simulation results show that the role of relay ramp as conduits for fluid flow is more pronounced in models built using TGS than that in deterministic models.

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.

In seismic reservoir characterization one aims to derive petrophysical properties of hydrocarbon reservoir conditioned to both available well-log and seismic reflection data. In this paper we present a new global pre-stack seismic AVO inversion methodology that inverts pre-stack seismic data directly for density, p-wave velocity, s-wave velocity and facies models. The retrieved subsurface models are able to reproduce the high vertical resolution of the well-log data with the spatial coverage of the seismic data. This new approach is based on an iterative genetic algorithm where the perturbation is done recurring to geostatistical simulation and co-simulation. The inverted facies model is an intrinsic part of the solution and represents the link between the elastic models (observed seismic) and the subsurface geology.KeywordsGeostatistical inversionAVO inversionPre-stack seismic inversionElastic models.

This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models. It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data. Compared with existing geostatistics-based modeling methods, our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called Generative Adversarial Networks (GANs). GANs couple a generator with a discriminator and each uses a deep Convolutional Neural Network (CNN). The networks are trained in an adversarial manner until the generator can create "fake" images that the discriminator cannot distinguish from "real" images. We extend the original GAN approach to 3D geological modeling at the reservoir scale. The GANs are trained using a library of 3D facies models. Once the GANs have been trained, they can generate a variety of geologically realistic facies models constrained by well data interpretations. This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends. The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods, which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.

Seismic inversion methods are routinely used to estimate attributes such as P-impedance, S-impedance, density, P-wave and S-wave velocity, and elastic impedances from seismic and well log data. These attributes help to understand subsurface lithology and fluid content. There are several types of seismic inversion methods available in which model-based inversion has got more attention. In the present study, the model-based inversion and geostatistical methods namely probabilistic neural network (PNN), multi-layer feed-forward neural network (MLFN) and radial basis function neural network (RBFNN) are implemented to estimate acoustic impedance, porosity, density, gamma-ray volume and P-wave velocity in the inter-well region of the F-3 block, Netherlands. The aim of the study is to predict a number of petrophysical parameters and demonstrate how they can be used to interpret seismic reflection data. A comparative study is also performed to estimate the best suitable method to predict various petrophysical parameters. The assessment of inverted results demonstrates that the impedance in the region varies from 2550 m/s*g/cc to 6150 m/s/*g/cc, which is relatively low and indicates the presence of loose formation in the area. The correlation between synthetic seismic trace and initial seismic trace is estimated to be 0.98, and the synthetic relative error as 0.19, indicating excellent algorithm output. Further, the assessment of the geostatistical methods is carried out to predict petrophysical parameters in conjunction with Multi-Attribute Transform Analysis. The analysis shows that among the geostatistical methods, the PNN provides slightly better results. The petrophysical parameters that are estimated in this study are porosity, density, gamma-ray, and P-wave velocity volumes. These parameters reinforce the interpretation of seismic data, which is a significant step in any project of exploration and production. The technique is first implemented to the composite traces near to the well location, and the findings are analysed with well log data. Second, the entire seismic section is inverted for porosity, density, gamma-ray, and P-wave velocity after achieving satisfactory outcomes in the first step. The assessment demonstrates very high-resolution subsurface petrophysical parameters which strengthen the seismic data interpretation.

The field of interest is an anticlinal structure aligned Northwest-Southeast, about 31 km long and 10 km wide. The field was discovered in 1984. The major reservoir, M is further subdivided into M1, L1 and L2. It is a carbonate reservoir containing approximately 75% of the total field Stock Tank Oil Initially In-Place (STOIIP). Other reservoirs are Y, Z and R. The biggest uncertainty in preparing a Field Development Plan (FDP) for the field is the subsurface uncertainty due to limited data availability. Areal and vertical heterogeneity are evident even from the early production data. These came at no surprise as available core data, well testing data and other pressure data suggest the same.

Prosody is known to scaffold the learning of language, and thus understanding prosodic development is vital for language acquisition. The present study explored the unfolding prosody model of prosodic development (proposed in Frota’s et al. study in 2016) beyond early production data, to examine whether it predicted the development of early segmentation abilities. European Portuguese-learning infants aged between 5 and 17 months were tested in a series of word segmentation experiments. Developing prosodic structure was evidenced in word segmentation as proposed by the unfolding model: (i) a simple monosyllabic word shape crucially placed at a major prosodic edge was segmented first, before more complex word shapes under similar prosodic conditions; (ii) the segmentation of more complex words was easier at a major prosodic edge than in phrase-medial position; and (iii) the segmentation of complex words with an iambic pattern preceded the segmentation of words with a trochaic pattern. These findings demonstrated that word segmentation evolved with unfolding prosody, suggesting that the prosodic units developed in the unfolding process are used both as speech production planning units and to extract word-forms from continuous speech. Therefore, our study contributes to a better understanding of the mechanisms underlying word segmentation, and to a better understanding of early prosodic development, a cornerstone of language acquisition.

The Arbroath field is located in UKCS block 22/17 and produces 38o API oil from the Forties Sandstone turbidite reservoir at approximately 2500m TVDSS. Production began in 1990 and the field still produces significant oil volumes today.In 2006, a re-appraisal of 1993 and 2000 vintage 4D seismic data over the field was initiated to de-risk infill drilling targets. This included a petrophysical and petroacoustic review and 4D seismic re-processing:- Petrophysical log database QC and repair, facies modelling and shear log prediction using clustering to provide a quality log set for all key wells.- Quantifying the seismic response to reservoir sand presence and fluid fill. Quantifying the dynamic seismic response associated with changes in OWC, oil saturation and fluid pressure due to continued production using seismic forward modelling techniques. Selecting optimum seismic elastic inversion products to highlight produced zones.- Improved 4D seismic images by using state-of-the-art time lapse processing technology.- Applying enhanced seismic processing to improve signal levels in seismic AVO volumes, leading to improved relative fluid impedance volumes for better delineation of by-passed oil.- Predicting continuous rock strength profiles along infill well paths, performing sand stability and in-situ stress analysis to optimise preferred perforation placement and orientation.This poster presents the integrated progression from seismic data review with petrophysical and petroacoustic analysis leading to identification of un-swept oil, and assisting infill well placement and design. We highlight the innovative technology used during each of the stages.

Summary We present a methodology for predicting lithofacies from seismic amplitudes, and apply it to a North Sea turbidite system. We define seismic lithofacies as seismic scale sedimentary units with characteristic rock physics properties. Sonic, gamma ray and density logs are used as training data in a multivariate statistical analysis to objectively determine seismic lithofacies. We classify lithofacies for 7 wells in the area, and create cumulative distribution functions (cdfs) of seismic properties for each facies. Pore fluid variations are accounted for b y applying Biot-Gassmann theory. The well log derived cdfs show that unconsolidated thick-bedded clean sands with water, plane laminated thick-bedded sands with oil, and pure shales have very similar acoustic impedance distributions. However, the Vp/Vs-ratio resolves these ambiguities. We therefore conduct AVO analysis to predict seismic lithofacies from seismic data. We assess uncertainties in AVO response related to the inherent natural variability of each lithofacies using a Monte-Carlo technique. AVO probability plots show that there are overlaps between different facies, but the most likely responses for each facies are nicely separated. Zero-offset reflectivity versus AVO-gradient bivariate probability plots are created to better assess the overlap between the different facies. We then analyze real CDPgathers at several well locations, and successfully predict the seismic lithofacies indicated by the log data. This demonstrates the feasibility of AVO analysis t o predict seismic lithofacies. By analyzing a selected 2-D AVO attribute section across the submarine fan lobe, we successfully predict the transition from water-saturated marginal interbedded sand-shales to oil-saturated thickbedded sands in the center of the lobe/lobe-channel. In general, we find that the turbidite system is a pointsourced sub-marine fan with cemented clean sands indicated in the feeder-channel, uncemented clean sands in the lobe-channels, and interbedded sand-shale facies and shaly sands in interchannel and marginal areas.

Deep learning is increasingly being applied in many aspects of seismic processing and interpretation. Here, we look at a deep convolutional neural network approach to multiclass seismic lithofacies characterization using well logs and seismic data. In particular, we focus on network performance and hyperparameter tuning. Several hyperparameter tuning approaches are compared, including true and directed random search methods such as very fast simulated annealing and Bayesian hyperparameter optimization. The results show that improvements in predictive capability are possible by using automatic optimization compared with manual parameter selection. In addition to evaluating the prediction accuracy's sensitivity to hyperparameters, we test various types of data representations. The choice of input seismic data can significantly impact the overall accuracy and computation speed of the optimized networks for the classification challenge under consideration. This is validated on a 3D synthetic seismic lithofacies example with acoustic and lithologic properties based on real well data and structure from an onshore oil field.

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.

SummaryStatistical pattern recognition, particularly the neural network approach, has found many applications in reservoir characterization, enabling the use of multi-variate, imprecise and uncertain reservoir data. Geostatistics is a well-established field for 3D spatial modeling and uncertainty quantification of the reservoir facies and petro-physical properties. In this paper we present a theoretical and practical framework for developing and applying pattern recognition tools within the traditional geostatistical framework. We show that the power of soft computing tools within a geostatistical framework allows the modeler to make maximum use of the reservoir data. Geostatistics aims at integrating geophysical and reservoir engineering data, yet at the same time honoring geological continuity information provided by well data or by analog outcrop information. However, the traditional geostatistical framework does not allow an easy integration of "non-linear" reservoir data. Due to the nature of the governing physical laws, seismic amplitude data and production history data both exibit a non-linear and multiple point relationship with petrophysical properties such as porosity and permeability. In this paper we show how statistical pattern recognition tools can be integrated into traditional and novel geostastical simulation methods in order to deal with the imprecise and non-linear aspects of reservoir data. Probabilistic type neural networks such as the proposed logistic regression network are ideal tools to model the probabilistic relation between reservoir data and reservoir properties. The output of these types of neural networks is a conditional probability, rather than the single estimate provided by more traditional neural networks. A framework is presented where these networks can be integrated within any geostatistical simulation algorithm. We provide two examples of this novel approach. First we show how neural networks axe trained to build a non-linear relation between seismic amplitude data and reservoir facies. The trained neural network is used to constrain a fluvial reservoir to seismic amplitude data. A second example shows the worth of using neural networks in understanding and calibrating the non-linear relationship between the permeability heterogeneity and well test response data. The resultant neural network calibrated relationship is then used to condition multiple reservoir models to well test data using an iterative Gaussian simulation method.

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 attributes often provide support for identification of several important parameters that could be applied in geomodelling for constraining reservoir properties such as porosity, permeability and fluid saturation. Additional insight can be gained as a way of detecting depositional features and geometries, and improve qualitative (e.g., data quality, seismic facies mapping) and quantitative analyses (e.g., net sand, porosity prediction). The case study is a deep gas bearing Jurassic fluvial channel sandstone reservoir in an onshore field in Kazakhstan, Central Asia. The data set is scarce, with only a few wells penetrating the reservoir. The available 3D seismic data is weak to a reasonable quality, and an attempt was carried out to use a series of attributes for channel identification and imaging. After testing several attributes, only two of them have shown good results: Generalized Spectral Decomposition (GSD) and Variance. Geobody extraction was also tried, but the seismic quality was not sufficient enough to further pursue such methodology. To overcome this issue, a joint approach for channels detection and extraction was tested by looking at the variance attribute in time slices and applying a workflow for an alternative way. With this technique (Alternative way for extracting geobodies), the facies property was generated in zones where the information was limited. The implementation of this approach, together with core facies interpretation has allowed a way of constraining other properties (e.g. porosity) and undoubtedly increased the robustness of the static model. Such model will support a more accurate estimation of HIIP volumes, future decisions on the most proper development plan and also investment decisions. The alternative way for extracting geobodies was tested in two channels examples and for different zones in Jurassic reservoir with success. The described workflow helped in channel extraction and it can be used in facies modelling when the reservoir data set is scarce. A more robust facies model can be generated by combining the seismic attribute results with well data.