Porosity estimation in the Fort Worth Basin constrained by 3D seismic attributes integrated in a sequential Bayesian simulation framework

The accurate inference of reservoir properties such as porosity and permeability is crucial in reservoir characterization for oil and gas exploration and production as well as for other geologic applications. In most cases, direct measurements of those properties are done in wells that provide high vertical resolution but limited lateral coverage. To fill this gap, geophysical methods can often offer data with dense 3D coverage that can serve as proxy for the variable of interest. All the information available can then be integrated using multivariate geostatistical methods to provide stochastic or deterministic estimate of the reservoir properties. Our objective is to generate multiple scenarios of porosity at different scales, considering four formations of the Fort Worth Basin altogether and then restricting the process to the Marble Falls limestones. Under the hypothesis that a statistical relation between 3D seismic attributes and porosity can be inferred from well logs, a Bayesian sequential simulation (BSS) framework proved to be an efficient approach to infer reservoir porosity from an acoustic impedance cube. However, previous BBS approaches only took two variables upscaled at the resolution of the seismic data, which is not suitable for thin-bed reservoirs. We have developed three modified BSS algorithms that better adapt the BSS approach for unconventional reservoir petrophysical properties estimation from deterministic prestack seismic inversion. A methodology that includes a stochastic downscaling procedure is built and one that integrates two secondary downscaled constraints to the porosity estimation process. Results suggest that when working at resolution higher than surface seismic, it is better to execute the workflow for each geologic formation separately.

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Acquisition of small surgical 3D seismic surveys in urban areas within the Fort Worth Basin

The Leading Edge ◽

10.1190/1.2542447 ◽

2007 ◽

Vol 26 (2) ◽

pp. 176-179

Author(s):

Thomas D. Bowman ◽

Wayne “Woody” Woodside ◽

Steve Culpepper

Keyword(s):

Urban Areas ◽

Fort Worth ◽

3D Seismic ◽

Seismic Surveys ◽

Fort Worth Basin

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

Interpretation ◽

10.1190/int-2020-0108.1 ◽

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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Integrated reservoir characterization: Beyond tomography

Geophysics ◽

10.1190/1.1443771 ◽

1995 ◽

Vol 60 (2) ◽

pp. 354-364 ◽

Cited By ~ 3

Author(s):

Larry Lines ◽

Henry Tan ◽

Sven Treitel ◽

John Beck ◽

Richard Chambers ◽

...

Keyword(s):

History Matching ◽

Preferential Flow ◽

Reservoir Characterization ◽

Seismic Anisotropy ◽

Flow Direction ◽

Geophysical Methods ◽

Reservoir Properties ◽

Traveltime Tomography ◽

Sonic Log ◽

Primary Water

In 1992, there was a collaborative effort in reservoir geophysics involving Amoco, Conoco, Schlumberger, and Stanford University in an attempt to delineate variations in reservoir properties of the Grayburg unit in a West Texas [Formula: see text] pilot at North Cowden Field. Our objective was to go beyond traveltime tomography in characterizing reservoir heterogeneity and flow anisotropy. This effort involved a comprehensive set of measurements to do traveltime tomography, to image reflectors, to analyze channel waves for reservoir continuity, to study shear‐wave splitting for borehole stress‐pattern estimation, and to do seismic anisotropy analysis. All these studies were combined with 3-D surface seismic data and with sonic log interpretation. The results are to be validated in the future with cores and engineering data by history matching of primary, water, and [Formula: see text] injection performance. The implementation of these procedures should provide critical information on reservoir heterogeneities and preferential flow direction. Geophysical methods generally indicated a continuous reservoir zone between wells.

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Applications of quantitative prestack seismic analysis to unconventional resource play characterization in the Permian/Delaware Basin

The Leading Edge ◽

10.1190/tle38020106.1 ◽

2019 ◽

Vol 38 (2) ◽

pp. 106-115 ◽

Cited By ~ 2

Author(s):

Phuong Hoang ◽

Arcangelo Sena ◽

Benjamin Lascaud

Keyword(s):

Hydraulic Fracturing ◽

Seismic Data ◽

Reservoir Characterization ◽

Seismic Analysis ◽

Seismic Inversion ◽

Reservoir Properties ◽

Yield Attributes ◽

Horizontal Drilling ◽

Delaware Basin ◽

Unconventional Resource

The characterization of shale plays involves an understanding of tectonic history, geologic settings, reservoir properties, and the in-situ stresses of the potential producing zones in the subsurface. The associated hydrocarbons are generally recovered by horizontal drilling and hydraulic fracturing. Historically, seismic data have been used mainly for structural interpretation of the shale reservoirs. A primary benefit of surface seismic has been the ability to locate and avoid drilling into shallow carbonate karsting zones, salt structures, and basement-related major faults which adversely affect the ability to drill and complete the well effectively. More recent advances in prestack seismic data analysis yield attributes that appear to be correlated to formation lithology, rock strength, and stress fields. From these, we may infer preferential drilling locations or sweet spots. Knowledge and proper utilization of these attributes may prove valuable in the optimization of drilling and completion activities. In recent years, geophysical data have played an increasing role in supporting well planning, hydraulic fracturing, well stacking, and spacing. We have implemented an integrated workflow combining prestack seismic inversion and multiattribute analysis, microseismic data, well-log data, and geologic modeling to demonstrate key applications of quantitative seismic analysis utilized in developing ConocoPhillips' acreage in the Delaware Basin located in Texas. These applications range from reservoir characterization to well planning/execution, stacking/spacing optimization, and saltwater disposal. We show that multidisciplinary technology integration is the key for success in unconventional play exploration and development.

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Introduction to this special section: Rock physics

The Leading Edge ◽

10.1190/tle38050332.1 ◽

2019 ◽

Vol 38 (5) ◽

pp. 332-332

Author(s):

Yongyi Li ◽

Lev Vernik ◽

Mark Chapman ◽

Joel Sarout

Keyword(s):

Reservoir Characterization ◽

Model Building ◽

Seismic Analysis ◽

Focal Point ◽

Special Section ◽

Rock Physics ◽

Seismic Inversion ◽

Reservoir Properties ◽

Model Studies ◽

Experimental Rock

Rock physics links the physical properties of rocks to geophysical and petrophysical observations and, in the process, serves as a focal point in many exploration and reservoir characterization studies. Today, the field of rock physics and seismic petrophysics embraces new directions with diverse applications in estimating static and dynamic reservoir properties through time-variant mechanical, thermal, chemical, and geologic processes. Integration with new digital and computing technologies is gradually gaining traction. The use of rock physics in seismic imaging, prestack seismic analysis, seismic inversion, and geomechanical model building also contributes to the increase in rock-physics influence. This special section highlights current rock-physics research and practices in several key areas, namely experimental rock physics, rock-physics theory and model studies, and the use of rock physics in reservoir characterizations.

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Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-020-01013-0 ◽

2020 ◽

Author(s):

Amir Abbas Babasafari ◽

Shiba Rezaei ◽

Ahmed Mohamed Ahmed Salim ◽

Sayed Hesammoddin Kazemeini ◽

Deva Prasad Ghosh

Keyword(s):

Acoustic Impedance ◽

Reservoir Characterization ◽

Seismic Inversion ◽

Reservoir Properties ◽

Well Location ◽

The Neural Network ◽

Machine Learning Approach ◽

Lithofacies Classification ◽

Elastic Inversion ◽

Significant Match

Abstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or Vp/Vs ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable.

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Applying 3D Seismic Technology to Better Understand the Mechanical Variability of the Lower Barnett Shale in Southern Montague and Northern Wise Counties, Fort Worth Basin.

10.1190/urtec2013-033 ◽

2013 ◽

Author(s):

Andy Stephens

Keyword(s):

Barnett Shale ◽

Fort Worth ◽

3D Seismic ◽

Fort Worth Basin

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IMAGING ADDITIONAL OIL AND GAS RESERVES, INTEGRATING WELL PRODUCTION DATA AND 3D SEISMIC AVO CLASSIFICATION TO IMAGE REMAINING OIL AND GAS RESERVES—A CASE STUDY ON THE NGATORO FIELD, TARANAKI, NEW ZEALAND

The APPEA Journal ◽

10.1071/aj00038 ◽

2001 ◽

Vol 41 (1) ◽

pp. 679

Author(s):

S. Reymond ◽

E. Matthews ◽

B. Sissons

Keyword(s):

New Zealand ◽

Oil And Gas ◽

Seismic Facies ◽

Bright Spot ◽

Production Data ◽

3D Seismic ◽

Reservoir Model ◽

Reservoir Properties ◽

Well Production

This case study illustrates how 3D generalised inversion of seismic facies for reservoir parameters can be successfully applied to image and laterally predict reservoir parameters in laterally discontinuous turbiditic depositional environment where hydrocarbon pools are located in complex combined stratigraphic-structural traps. Such conditions mean that structural mapping is inadequate to define traps and to estimate reserves in place. Conventional seismic amplitude analysis has been used to aid definition but was not sufficient to guarantee presence of economic hydrocarbons in potential reservoir pools. The Ngatoro Field in Taranaki, New Zealand has been producing for nine years. Currently the field is producing 1,000 bopd from seven wells and at three surface locations down from a peak of over 1,500 bopd. The field production stations have been analysed using new techniques in 3D seismic imaging to locate bypassed oils and identify undrained pools. To define the objectives of the study, three questions were asked:Can we image reservoir pools in a complex stratigraphic and structural environment where conventional grid-based interpretation is not applicable due to lack of lateral continuity in reservoir properties?Can we distinguish fluids within each reservoir pools?Can we extrapolate reservoir parameters observed at drilled locations to the entire field using 3D seismic data to build a 3D reservoir model?Using new 3D seismic attributes such as bright spot indicators, attenuation and edge enhancing volumes coupled with 6 AVO (Amplitude Versus Offset) volumes integrated into a single class cube of reservoir properties, made the mapping of reservoir pools possible over the entire data set. In addition, four fluid types, as observed in more than 20 reservoir pools were validated by final inverted results to allow lateral prediction of fluid contents in un-drilled reservoir targets. Well production data and 3D seismic inverted volume were later integrated to build a 3D reservoir model to support updated volumetrics reserves computation and to define additional targets for exploration drilling, additional well planning and to define a water injection plan for pools already in production.

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