Estimating tilted fracture weaknesses from azimuthal differences in seismic amplitude data

Multiscale frequency-domain seismic inversion for fracture weakness

PP-wave azimuthal elastic impedance (EI) data inverted from azimuthal prestack seismic reflection data can be theoretically and practically used for fluid- and fracture-property discrimination in naturally porous and fluid-saturated fractured reservoirs within the exploration seismic frequency band. From the perspective of the low-frequency Biot-Gassmann theory, Russell’s fluid indicator can be stably applied to fluid identification in hard-rock fractured reservoirs. Based on the low-frequency anisotropic poroelasticity theory, we first derive an azimuthal PP-wave reflection coefficient and an azimuthal EI equation in terms of Russell’s fluid indicator (a combination of acoustic impedances and dry-rock velocity ratio without the effect of squirt flow in the seismic frequency range) and two fracture indicators (i.e., normal and tangential fracture weaknesses). Using azimuthal differences in EI data, we then develop a three-step inversion method of EI difference versus angle and azimuth. It can be used accurately to estimate the fluid and fracture indicators and characterize the sensitive properties of saturated fractured porous reservoirs. Noisy synthetic data are used to verify the stability and robustness of the proposed hierarchical impedance inversion approach. A real data set acquired over a gas-bearing fractured reservoir is also used to obtain the sensitive indicators of the fluid and fracture properties, which agree well with the well-log data. Thus, our hierarchical inversion approach provides a more straightforward and efficient manner to characterize the porous and fluid-saturated fractured reservoirs.

ABSTRACTBy analogy with P‐ and S‐wave impedances, the product of Young's modulus and density can be termed as Young's impedance, which indicates the rock lithology and brittleness of unconventional hydrocarbon reservoirs. Poisson's ratio is also an effective indicator of rock brittleness and fluid property of unconventional reservoirs, and fracture weaknesses indicate the fracture properties (fracturing intensity and fracture fillings) in fracture‐induced unconventional reservoirs. We aim to simultaneously estimate the Young's impedance, Poisson's ratio and fracture weaknesses from wide‐azimuth surface seismic data in a fracture‐induced shale gas reservoir, and use the horizontal transversely isotropic model to characterize the fractures. First, the linearized PP‐wave reflection coefficient in terms of Young's impedance, Poisson's ratio, density and fracture weaknesses is derived for the case of a weak‐contrast interface separating two weakly horizontal transversely isotropic media. In addition, an orthorhombic anisotropic case is also discussed in this paper. Then a Bayesian amplitude variation with incident angle and azimuth scheme with a model constraint is used to stably estimate Young's impedance, Poisson's ratio and fracture weaknesses with only PP‐wave azimuthal seismic data. The proposed approach is finally demonstrated on both synthetic and real data sets with reasonable results.

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.

Effective reservoir characterization approaches aim to integrate all available and reliable sources of data to mitigate the effects of uncertainty of reservoir parameters and have a robust reservoir model(s). Reservoir permeability is one of the most critical, and usually, uncertain parameters in reservoir simulation models. The permeability model has a direct impact on production forecasts and consequently the economics of decisions made based on reservoir models. Having a calibrated permeability model is a challenging task. In most cases, the permeability model undergoes several modifications to achieve a history-matched reservoir model that honors the historical pressure and production data. The objective of this work is to show a workflow that integrates two different types of data sources to help calibrate the permeability of reservoir models. The proposed approach relies on adjusting the permeability of reservoir models by integrating seismic amplitude values and data from wells’ pressure transient analyses. The seismic amplitude data in the vicinity of a candidate well is correlated with permeability obtained from recorded pressure transient data in a systematic way. The generated correlation is then applied to the entire reservoir model in order to calibrate the permeability value of each grid block in the model. The application of the proposed approach on actual field data is also presented. As a result, the seismic and pressure transient data both can make valuable contributions towards calibrating geo-cellular 3D reservoir models at their respective and different scales.

Fractures and tectonic settings cause azimuthal anisotropy in reservoirs. Recognizing the fracture model from the seismic data is a useful tool for identifying the productive zone in the reservoirs. We applied azimuthal velocity analysis in seismic processing to improve image quality and to estimate anisotropic model parameters. Using azimuthal residual moveout analysis, we predicted the direction of azimuthal anisotropy in the reservoir, and found that the results are consistent with fracture orientations obtained from the image logs in the reservoirs. Bayes’ theorem, and a cascaded procedure in least-squares inversion, matching observed amplitudes to linearized Zoeppritz equations, were used to estimate the elastic moduli in a first step and normal and tangential fracture weaknesses in a second step. We used laboratory experiments on core samples to validate the first step inversion results. We found that the propagation wavelet varied in space and reflection time, and so a library of extracted wavelets in the time-frequency domain was used for seismic inversion. Maps of computed fracture fluid index and the estimated fracture weaknesses help to visualize the role of fractures in reservoir productivity and revealed a consistency with the seismic peak frequency attribute in identifying zones of highly compliant fracture fill. The estimated fracture model demonstrates a good fit with the fractures seen in the available core samples and implies that the fracture fluid index is a useful attribute for determining the productive zones in the reservoir.

Acceleration of stable TTI P-wave reverse-time migration with GPUs

Interpretation of three-dimensional (3D) seismic data from the northern part of central Saudi Arabia allowed mapping of the Permo-Carboniferous Unayzah Formation's aeolian sand deposits and fluvial channels. These types of depositional environments have been associated frequently with better reservoir-quality sandstones within Saudi Arabia. The depositional setting of the Unayzah reservoirs can be highly variable and range from glacial, to lacustrine, to meandering fluvial, to aeolian, to braided stream. Seismic amplitude data were instrumental in mapping a truncation of the upper Unayzah reservoir against the overlying Pre-Khuff Unconformity. The use of seismic attributes, such as coherence and curvature, indicated the existence of faulting and a fluvial channel network at the top of the Unayzah Formation. The integration of well and seismic amplitude data, via acoustic impedance inversion, revealed the direction of potentially higher porosity fairways within the channel system. The structural and depositional architecture depicted in this study is being appraised through drilling of an exploration well.

This paper proposes a method for quantitative integration of seismic (elastic) anisotropy attributes with reservoir performance data as an aid in characterization of systems of natural fractures in hydrocarbon reservoirs. This method is demonstrated through application to history matching of reservoir performance using synthetic test cases. Discrete Feature Network (DFN) modeling (Dershowitz et al.1) is a powerful tool for developing field-wide stochastic realizations of fracture networks in petroleum reservoirs. Such models are typically well conditioned in the vicinity of the wellbore through incorporation of core data, borehole imagery, and pressure transient data. Model uncertainty generally increases with distance from the borehole. Threedimensional seismic data provides uncalibrated information throughout the inter-well space. Some elementary seismic attributes such as horizon curvature and impedance anomalies have been used to guide estimates of fracture trend and intensity (P32) (Dershowitz and Herda2) in DFN modeling through geostatistical calibration with borehole and other data. However, these attributes often provide only weak statistical correlation with fracture system characteristics. The presence of a system of natural fractures in a reservoir induces elastic anisotropy that can be observed in seismic data. Elastic attributes such as azimuthally dependent normal moveout velocity (ANMO), reflection amplitude versus azimuth (AVAZ), and shear wave bi-refringence can be inverted from 3D seismic data. Anisotropic elastic theory provides physical relationships among these attributes and fracture system properties such as trend and intensity. Effective elastic media models allow forward modeling of elastic properties for fractured media. A technique has been developed in which both reservoir performance data and seismic anisotropic attributes are used in an objective function for gradient-based optimization of selected fracture system parameters. The proposed integration method involves parallel workflows for effective elastic and effective permeability media modeling from an initial DFN estimate of the fracture system. The objective function is minimized through systematic updates of selected fracture population parameters. Synthetic data cases show that 3D seismic attributes contribute significantly to reduction of ambiguity in estimates of fracture system characteristics in the inter-well rock mass. The method will benefit enhanced oil recovery (EOR) program planning and management, optimization of horizontal well trajectory and completion design, and borehole stability studies.

Based on a model of attenuative cracked rock, we have derived a simplified and frequency-dependent stiffness matrix associated with (1) a rock volume containing aligned and partially saturated cracks and (2) a new indicator of oil-bearing fractured reservoirs, which is related to pressure relaxation in cracked rocks and influenced by fluid viscosity and saturation. Starting from the mathematical form of a perturbation in this stiffness matrix across a reflecting interface separating two attenuative cracked media, we set up a linearized P-wave to P-wave reflection coefficient as an azimuthally and frequency-dependent function of dry rock elastic properties, dry fracture weaknesses, and the new indicator. By varying this reflection coefficient with azimuthal angle, we derive a further expression referred to as the quasidifference in elastic impedance, or QδEI, which is primarily affected by the dry fracture weaknesses and the new indicator. An inversion approach is established to use differences in frequency components of seismic amplitudes to estimate these weaknesses and the indicator based on the derived QδEI. In synthetic inversion tests, we determine that the approach produces interpretable parameter estimates in the presence of data with a moderate signal-to-noise ratio (S/N). Testing on a real data set suggests that reliable fracture weakness and indicator are generated by the approach; fractured and oil-bearing reservoirs are identified through a combination of the dry fracture weakness and the new indicator.

Bottom-simulating reflections (BSRs) that sometimes mark the base of the gas hydrate stability zone in marine sediments are often identified based on the reverse polarity reflections that cut across stratigraphic layering in seismic amplitude data. On the northern U.S. Atlantic margin (USAM) between Cape Hatteras and Hudson Canyon, legacy seismic data have revealed pronounced BSRs south of the deepwater extension of Hudson Canyon and more subtle ones from offshore Delaware south to Cape Hatteras, where the reflections sometimes follow stratigraphic layering. Using high-resolution seismic data acquired during the 2018 Mid-Atlantic Resource Imaging Experiment and a supervised neural net, we identify seismic features associated with gas hydrates and/or the top of gas between Hudson Canyon and Cape Hatteras. Using seismic attributes especially sensitive to the presence of gas, we train a neural network algorithm on seismic data from an area with strong BSRs and then apply the model to the rest of the data set. The results indicate that gas hydrate and/or shallow free gas are significantly more widespread on the northern part of the USAM than previously known. Seismic indicators of gas extend landward from the 2000 m isobath to the upper continental slope in sectors with (offshore Virginia) and, to a lesser extent, without (offshore New Jersey) pervasive upper slope methane seeps. Higher sand content and intermediate sediment thickness, factors related to the container size and gas charge in a petroleum systems framework, are associated with more robust gas indicators.

Depth imaging with anisotropic models has been shown to deliver more geologically plausible models and accurate images. Deriving parameters that describe the anisotropic properties of the subsurface requires incorporating well information. There are, however, vast exploration areas around the world with very limited to no well control that do require high-quality anisotropic imaging to allow adequate interpretation of deeper targets below complex structures. We present a general workflow for building tilted transversely isotropic (TTI) models in areas of very limited well control. We incorporate published knowledge of the area and analysis of data anellipticity, with a derivation of Thomsen’s δ in wells from adjacent areas. We use single regional Thomsen’s parameter trends hung from the water bottom. Finally, we present a case study that applies this workflow to seismic data from the Kwanza basin offshore Angola where TTI models were built over more than 12000 km2 in two adjacent areas. By accounting for TTI anisotropy, we have produced geologically plausible and interpretable images from relatively old narrow-azimuth streamer data with only moderate offsets of 4.8 km.

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.

We present an integrated study of fracture detection by analyzing the seismic anisotropy in the 3D P-wave seismic data from Caidamu Basin in Northwest China. The study area lies on a big anticline which runs from southeast to northwest with the area around 400 km2, and each flank of the anticline develops a reverse fault. The target layer is a fractured gas reservoir at a depth of around 3 km just under the anticline. We first study the fracture characteristics from outcrop analogues, core samples and well logs. We then analyze the coherence and curvature attributes from post-stack seismic data, and azimuthal variation of amplitude and traveltime from pre-stack seismic data. After a careful and integrated analysis, we find that fractures are widespread and regularly distributed in the middle of study area along the anticline strike, and form good pathways and store space for fluid. The final results estimated using azimuthal analysis of pre-stack seismic amplitude data are consistent with results of core and log analysis at the well locations, which extended our knowledge of the fracture distribution in the reservoir.

The prediction of highly heterogeneous reservoir parameters from seismic amplitude data is a major challenge. Seismic attribute analysis can enhance the tracking of subtle stratigraphic features. It is challenging to investigate these subtle features, including channel systems, with conventional-amplitude seismic data. Over the past few years, the use of machine learning (ML) to analyze multiple seismic attributes has enhanced the facies analysis by mapping patterns in seismic data. The purpose of this research was to assess the efficiency of an unsupervised self-organizing map (SOM) approach supported by multi-attribute analysis that could improve gas channel detection and seismic facies classification in Serpent Field, offshore Nile Delta, Egypt. As well as evaluates the importance of several available seismic attributes in reservoir characterization rather than analyzing individual attribute volumes. In this study, the single attribute (spectral decomposition attribute) highlighted the gas channel spatial distribution using three distinct frequency magnitude values. Subsequently, we employ principal component analysis (PCA) as an attribute selection method, discovering that combining seismic attributes such as sweetness, envelope, spectral magnitude, and spectral voice as input for SOM reflects an effective method to determine facies. The clustering results distinguish between shale, shaly sand, wet sand, and gas-saturated sand and identify gas–water contact on a 2D topological map (SOM), where each pattern indicates certain facies. This is achieved by associating the SOM outputs with lithofacies determined from petrophysical logs. Reducing exploration and development risk and empowering the geoscientist to generate a more precise interpretation are the ultimate objectives of this multi-attribute analysis.