Estimating tilted fracture weaknesses from azimuthal differences in seismic amplitude data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. R135-R146
Author(s):  
Huaizhen Chen ◽  
Tiansheng Chen ◽  
Kristopher A. Innanen
Keyword(s):  
Reflection Coefficient ◽  
Tilt Angle ◽  
Seismic Data ◽  
Fractured Reservoirs ◽  
Transverse Isotropy ◽  
Elastic Response ◽  
Moderate Degree ◽  
Fractured Reservoir ◽  
Seismic Amplitude ◽  
Amplitude Data

Tilted transverse isotropy (TTI) provides a useful model for the elastic response of a medium containing aligned fractures with a symmetry axis oriented obliquely in the vertical and horizontal coordinate directions. Robust methods for determining the TTI properties of a medium from seismic observations to characterize fractures are sought. Azimuthal differencing of seismic amplitude data produces quantities that are particularly sensitive to TTI properties. Based on the linear slip fracture model, we express the TTI stiffness matrix in terms of the normal and tangential fracture weaknesses. Perturbing stiffness parameters to simulate an interface separating an isotropic medium and a TTI medium, we derive a linearized P-to-P reflection coefficient expression in which the influence of tilt angle and fracture weaknesses separately emerge. We formulate a Bayesian inversion approach in which amplitude differences between seismic data along two azimuths, interpreted in terms of the reflection coefficient approximation, are used to determine fracture weaknesses and tilt angle. Tests with simulated data confirm that the unknown parameter vector involving fracture weakness and tilted fracture weaknesses is stably estimated from seismic data containing a moderate degree of additive Gaussian noise. The inversion approach is applied to a field surface seismic data acquired over a fractured reservoir; from it, interpretable tilted fracture weaknesses, consistent with expected reservoir geology, are obtained. We determine that our inversion approach and the established inversion workflow can produce the properties of systems of tilted fractures stably using azimuthal seismic amplitude differences, which may add important information for characterization of fractured reservoirs.

Resolving subtle stratigraphic features using spectral ridges and phase residues

2013 ◽  
Vol 1 (1) ◽  
pp. SA93-SA108 ◽  
Author(s):  
Oswaldo Davogustto ◽  
Marcílio Castro de Matos ◽  
Carlos Cabarcas ◽  
Toan Dao ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Thin Layers ◽  
Earth Model ◽  
Well Log ◽  
Anadarko Basin ◽  
Seismic Amplitude ◽  
West Texas ◽  
Amplitude Data ◽  
Using Data ◽  
Extract Information

Seismic interpretation is dependent on the quality and resolution of seismic data. Unfortunately, seismic amplitude data are often insufficient for detailed sequence stratigraphy interpretation. We reviewed a method to derive high-resolution seismic attributes based upon complex continuous wavelet transform pseudodeconvolution (PD) and phase-residue techniques. The PD method is based upon an assumption of a blocky earth model that allowed us to increase the frequency content of seismic data that, for our data, better matched the well log control. The phase-residue technique allowed us to extract information not only from thin layers but also from interference patterns such as unconformities from the seismic amplitude data. Using data from a West Texas carbonate environment, we found out how PD can be used not only to improve the seismic well ties but also to provide sharper sequence terminations. Using data from an Anadarko Basin clastic environment, we discovered how phase residues delineate incised valleys seen on the well logs that are difficult to see on vertical slices through the original seismic amplitude.

scholarly journals Application of Artificial Neural Network in seismic reservoir characterization: a case study from Offshore Nile Delta

2021 ◽  
Author(s):  
Adel Othman ◽  
Mohamed Fathy ◽  
Islam A. Mohamed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Inversion Methods ◽  
Amplitude Data ◽  
Seismic Reservoir

AbstractThe Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results.

scholarly journals Seismic characterization of fractured reservoirs by focusing Gaussian beams

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. A23-A28 ◽  
Author(s):  
Yingcai Zheng ◽  
Xinding Fang ◽  
Michael C. Fehler ◽  
Daniel R. Burns
Keyword(s):  
Seismic Data ◽  
Fractured Reservoirs ◽  
Oil Production ◽  
Oil Field ◽  
Critical Parameters ◽  
Gaussian Beams ◽  
Fractured Reservoir ◽  
Fracture Orientation ◽  
Double Beam

Naturally fractured reservoirs occur worldwide, and they account for the bulk of global oil production. The most important impact of fractures is their influence on fluid flow. To maximize oil production, the characterization of a fractured reservoir at the scale of an oil field is very important. For fluid transport, the critical parameters are connectivity and transmittivity plus orientation. These can be related to fracture spacing, compliance, and orientation, which are the critical seismic parameters of rock physics models. We discovered a new seismic technique that can invert for the spatially dependent fracture orientation, spacing, and compliance, using surface seismic data. Unlike most seismic methods that rely on using singly scattered/diffracted waves whose signal-to-noise ratios are usually very low, we found that waves multiply scattered by fractures can be energetic. The direction information of the fracture multiply scattered waves contains fracture orientation and spacing information, and the amplitude of these waves gives the compliance. Our algorithm made use of the interference of two true-amplitude Gaussian beams emitted from surface source and receiver arrays that are extrapolated downward and focused on fractured reservoir targets. The double beam interference pattern provides information about the three fracture parameters. We performed a blind test on our methodology. A 3D model with two sets of orthogonal fractures was built, and a 3D staggered finite-difference method using the Schoenberg linear-slip boundary condition for fractures was used to generate the synthetic surface seismic data set. The test results showed that we were able to not only invert for the fracture orientation and spacing, but also the compliance field.

Coherence attribute applications on seismic data in various guises — Part 1

2018 ◽  
Vol 6 (3) ◽  
pp. T521-T529 ◽  
Author(s):  
Satinder Chopra ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Added Value ◽  
Karst Collapse ◽  
Additional Information ◽  
Seismic Amplitude ◽  
Input Amplitude ◽  
Amplitude Data ◽  
Decomposition Procedure ◽  
Band Spectra ◽  
Definition Of

The iconic coherence attribute is very useful for imaging geologic features such as faults, deltas, submarine canyons, karst collapse, mass-transport complexes, and more. In addition to its preconditioning, the interpretation of discrete stratigraphic features on seismic data is also limited by its bandwidth, where in general the data with higher bandwidth yield crisper features than data with lower bandwidth. Some form of spectral balancing applied to the seismic amplitude data can help in achieving such an objective so that coherence run on spectrally balanced seismic data yields a better definition of the geologic features of interest. The quality of the generated coherence attribute is also dependent in part on the algorithm used for its computation. In the eigenstructure decomposition procedure for coherence computation, spectral balancing equalizes each contribution to the covariance matrix, and thus it yields crisper features on coherence displays. There are other ways to modify the spectrum of the input data in addition to simple spectral balancing, including the amplitude-volume technique, taking the derivative of the input amplitude, spectral bluing, and thin-bed spectral inversion. We compare some of these techniques, and show their added value in seismic interpretation, which forms part of the more elaborate exercise that we had carried out. In other work, we discuss how different spectral components derived from the input seismic data allow interpretation of different scales of discontinuities, what additional information is provided by coherence computed from narrow band spectra, and the different ways to integrate them.

Shaly sand rock physics analysis and seismic inversion implication

2014 ◽  
Vol 54 (2) ◽  
pp. 503
Author(s):  
Adi Widyantoro ◽  
Matthew Saul
Keyword(s):  
Seismic Data ◽  
Oil And Gas ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Elastic Response ◽  
Seismic Amplitude ◽  
Cap Rock ◽  
Shaly Sand ◽  
Well Data ◽  
Stiffening Effect

The analysis of well data from the Enfield field of the Exmouth Sub-basin, WA, indicates that both cementation and pore-filling clay appear to have a stiffening effect on the reservoir sands. The elastic contrast between brine sand and the overlying shale is often small and the large amplitudes observed from seismic data are associated with hydrocarbon content. More detailed rock physics and depth trend analysis of elastic and petrophysical properties, however, indicate significant spatial variability in the cap rock shales across the field with different sand shale mixtures, causing changes in the elastic response of the rock. Areas where shales are softer produce weak seismic amplitude contrasts even with high hydrocarbon saturation; the amplitude response being similar to areas with stiffer shales and brine-filled sands. The variations in reservoir quality are, therefore, masked by the distribution of the brine, oil and gas, as well as the variations in the cap rock. The Enfield rock physics analysis provides an example of reducing amplitude ambiguity over lithology-fluid variation and improves the chance of successful interpretation of the results of seismic inversion.

A workflow to skeletonize faults and stratigraphic features

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. O57-O70 ◽  
Author(s):  
Jie Qi ◽  
Gabriel Machado ◽  
Kurt Marfurt
Keyword(s):  
Seismic Data ◽  
Fault Plane ◽  
Random Noise ◽  
Principal Component ◽  
Component Structure ◽  
Seismic Surveys ◽  
Seismic Amplitude ◽  
Amplitude Data ◽  
Seismic Reflectors

Improving the accuracy and completeness of subtle discontinuities in noisy seismic data is useful for mapping faults, fractures, unconformities, and stratigraphic edges. We have developed a workflow to improve the quality of coherence attributes. First, we apply principal component structure-oriented filtering to reject random noise and sharpen the lateral edges of seismic amplitude data. Next, we compute eigenstructure coherence, which highlights the stratigraphic and structural discontinuities. We apply a Laplacian of a Gaussian filter to the coherence attribute that sharpens the steeply dipping faults, attenuates the stratigraphic features parallel to the seismic reflectors, and skeletonizes the unconformity features subparallel to the reflectors. Finally, we skeletonize the filtered coherence attribute along with the fault plane. The filtered and skeletonized seismic coherence attribute highlights the geologic discontinuities more clearly and precisely. These discontinuous features can be color coded by their dipping orientation or as a suite of independent, azimuthally limited volumes, providing the interpreter a means of isolating fault sets that are either problematic or especially productive. We validate the effectiveness of our workflow by applying it to seismic surveys acquired from the Gulf of Mexico, USA, and the Great South Basin, New Zealand. The skeletonized result rejects noise and enhances discontinuities seen in the vertical and lateral directions. The corendering of the “fault” azimuth and the fault-dip magnitude exhibits the strengths of the discontinuities and their orientation. Finally, we compared our workflow with the results generated from the swarm intelligence and found our method to be better at tracking short faults and stratigraphic discontinuities.

scholarly journals Pitfalls and limitations in seismic attribute interpretation of tectonic features

2015 ◽  
Vol 3 (1) ◽  
pp. SB5-SB15 ◽  
Author(s):  
Kurt J. Marfurt ◽  
Tiago M. Alves
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Seismic Attribute ◽  
3D Data ◽  
Amplitude Data ◽  
Seismic Sections ◽  
Push Down ◽  
Data Coherence ◽  
Level Of Experience ◽  
Tectonic Features

Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint and velocity pull-up/push-down, as well as small processing and velocity errors in seismic imaging. As a result, the chance that an interpreter will suffer a pitfall is inversely proportional to his or her experience. Interpreters with a history of making conventional maps from vertical seismic sections will have previously encountered problems associated with acquisition, processing, and imaging. Because they know that attributes are a direct measure of the seismic amplitude data, they are not surprised that such attributes “accurately” represent these familiar errors. Less experienced interpreters may encounter these errors for the first time. Regardless of their level of experience, all interpreters are faced with increasingly larger seismic data volumes in which seismic attributes become valuable tools that aid in mapping and communicating geologic features of interest to their colleagues. In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and errors made in attribute computation by not accounting for structural dip. We evaluate these errors using 3D data volumes and find areas where present-day attributes do not provide the images we want.

Dense surface seismic data confirm non-double-couple source mechanisms induced by hydraulic fracturing

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. KS207-KS217 ◽  
Author(s):  
Jeremy D. Pesicek ◽  
Konrad Cieślik ◽  
Marc-André Lambert ◽  
Pedro Carrillo ◽  
Brad Birkelo
Keyword(s):  
Hydraulic Fracturing ◽  
Seismic Data ◽  
P Wave ◽  
Source Modeling ◽  
Moment Tensors ◽  
Amplitude Data ◽  
Double Couple ◽  
Source Mechanisms ◽  
Source Models ◽  
Favorable Conditions

We have determined source mechanisms for nine high-quality microseismic events induced during hydraulic fracturing of the Montney Shale in Canada. Seismic data were recorded using a dense regularly spaced grid of sensors at the surface. The design and geometry of the survey are such that the recorded P-wave amplitudes essentially map the upper focal hemisphere, allowing the source mechanism to be interpreted directly from the data. Given the inherent difficulties of computing reliable moment tensors (MTs) from high-frequency microseismic data, the surface amplitude and polarity maps provide important additional confirmation of the source mechanisms. This is especially critical when interpreting non-shear source processes, which are notoriously susceptible to artifacts due to incomplete or inaccurate source modeling. We have found that most of the nine events contain significant non-double-couple (DC) components, as evident in the surface amplitude data and the resulting MT models. Furthermore, we found that source models that are constrained to be purely shear do not explain the data for most events. Thus, even though non-DC components of MTs can often be attributed to modeling artifacts, we argue that they are required by the data in some cases, and can be reliably computed and confidently interpreted under favorable conditions.

Forecasting the Water Flooding for the Production Wells of a Gas Condensate Field with a Fractured Reservoir Type

2021 ◽  
Author(s):  
Pavel Dmitrievich Gladkov ◽  
Anastasiia Vladimirovna Zheltikova
Keyword(s):  
Produced Water ◽  
Fractured Reservoirs ◽  
Gas Condensate ◽  
Water Flooding ◽  
Fractured Reservoir ◽  
Well Performance ◽  
Integrated Monitoring ◽  
Field Development ◽  
Water Gas ◽  
Gas Ratio

Abstract As is known, fractured reservoirs compared to conventional reservoirs have such features as complex pore volume structure, high heterogeneity of the porosity and permeability properties etc. Apart from this, the productivity of a specific well is defined above all by the number of natural fractures penetrated by the wellbore and their properties. Development of fractured reservoirs is associated with a number of issues, one of which is related to uneven and accelerated water flooding due to water breakthrough through fractures to the wellbores, for this reason it becomes difficult to forecast the well performance. Under conditions of lack of information on the reservoir structure and aquifer activity, the 3D digital models of the field generated using the hydrodynamic simulators may feature insufficient predictive capability. However, forecasting of breakthroughs is important in terms of generating reliable HC and water production profiles and decision-making on reservoir management and field facilities for produced water treatment. Identification of possible sources of water flooding and planning of individual parameters of production well operation for the purpose of extending the water-free operation period play significant role in the development of these reservoirs. The purpose of this study is to describe the results of the hydrochemical monitoring to forecast the water flooding of the wells that penetrated a fractured reservoir on the example of a gas condensate field in Bolivia. The study contains data on the field development status and associated difficulties and uncertainties. The initial data were results of monthly analyses of the produced water and the water-gas ratio dynamics that were analyzed and compared to the data on the analogue fields. The data analysis demonstrated that first signs of water flooding for the wells of the field under study may be diagnosed through the monitoring of the produced water mineralization - the water-gas ratio (WGR) increase is preceded by the mineralization increase that may be observed approximately a month earlier. However, the data on the analogue fields shows that this period may be longer – from few months to two years. Thus, the hydrochemical method within integrated monitoring of development of a field with a fractured reservoir could be one of the efficient methods to timely adjust the well operation parameters and may extend the water-free period of its operation.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

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