Seismic attribute driven integrated characterization of the Woodford Shale in west-central Oklahoma

2013 ◽  
Vol 1 (2) ◽  
pp. SB85-SB96 ◽  
Author(s):  
Nabanita Gupta ◽  
Supratik Sarkar ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Attributes ◽  
Seismic Inversion ◽  
Organic Content ◽  
Tectonic Deformation ◽  
Well Performance ◽  
Woodford Shale ◽  
Seismic Attribute ◽  
Well Completion ◽  
West Central ◽  
Basin Floor

The organic-rich, silty Woodford Shale in west-central Oklahoma is a prolific resource play producing gas and liquid hydrocarbons. We calibrated seismic attributes and prestack inversion using well logs and core information within a seismic geomorphologic framework to define the overall basin architecture, major stratigraphic changes, and related variations in lithologies. Core measurements of elastic moduli and total organic content (TOC) indicated that the Woodford Shale can be broken into three elastic petrotypes important to well completion and hydrocarbon enrichment. Upscaling these measurements facilitates regional mapping of petrotypes from prestack seismic inversion of surface data. Seismic attributes highlight rugged topography of the basin floor of the Paleo Woodford Sea, which controls the lateral and vertical distribution of different lithofacies containing variable quantity of TOC as well as quartz, which controls brittleness. Depressions on the basin floor contain TOC-lean cherty lithofacies alternating with TOC-rich lithofacies, resulting in brittle-ductile rock couplets. In contrast, basin floor highs are characterized by overall TOC-rich ductile lithofacies. Seismic attributes illuminate complex post-Woodford tectonic deformation. The Woodford Shale is known to be naturally fractured on outcrop. Image log analysis in other shale plays showed a good correlation between such tectonic features and natural fractures. These features need to be correlated with well trajectories and production data to determine which hypothesized “fracture sets,” if any, improve well performance.

Characterization of mass transport deposits using seismic attributes: Upper Leonard Formation, Permian Basin

2019 ◽  
Vol 7 (4) ◽  
pp. SK19-SK32 ◽  
Author(s):  
Paritosh Bhatnagar ◽  
Sumit Verma ◽  
Ron Bianco
Keyword(s):  
Mass Transport ◽  
Mass Movement ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Seismic Survey ◽  
High Resistivity ◽  
Tectonic Deformation ◽  
Permian Basin ◽  
Midland Basin ◽  
Basin Floor

The Permian Basin is a structurally complex sedimentary basin with an extensive history of tectonic deformation. As the basin evolved through time, sediments dispersed into the basin floor from surrounding carbonate platforms leading to various mass movements. One such mass movement is observed on a 3D seismic survey in the Upper Leonard interval (Lower Permian) of the Midland Basin that is characteristic of a mass transport deposit (MTD). The 350 ft thick MTD mapped in the study area is 5 mi wide, extends up to 14 mi basinward, and covers only the translational and compressional regime of the mass movement. A unique sedimentary feature, unlike those observed previously, is mapped and interpreted as gravity spreading. MTDs have been extensively studied in the Delaware Basin of Permian-aged strata; however, only a few works have been published on the geomorphological expression of MTDs using seismic and seismic attributes to delineate the shape, size, and anatomy of this subsurface feature. The MTD in the study area exhibits an array of features including slide, slump, basal shear surface, and MTD grooves. In cross section, the MTD is characterized as chaotic with semitransparent reflectors terminating laterally against a coherent package of seismic facies, or the lateral wall. Sobel filter-based coherence, structural curvature, dip magnitude, and dip azimuth attributes are used to map thrust faults within the discontinuous MTD. Kinematic evidence provided by the Upper Spraberry isopach suggests that this MTD was sourced north of the Midland Basin and deposited on the basin floor fairway. Slope strata are interpreted from well-log analysis showing MTD as a mixture of carbonates and siliciclastics with a moderate to high resistivity response.

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.

scholarly journals Characterization of reservoir sands using 3D seismic attributes in the coastal swamp area of Niger Delta Basin

2021 ◽  
Author(s):  
Oluwatoyin Khadijat Olaleye ◽  
Pius Adekunle Enikanselu ◽  
Michael Ayuk Ayuk
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Seismic Attributes ◽  
Effective Porosity ◽  
High Porosity ◽  
Hydrocarbon Accumulation ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Niger Delta Basin

AbstractHydrocarbon accumulation and production within the Niger Delta Basin are controlled by varieties of geologic features guided by the depositional environment and tectonic history across the basin. In this study, multiple seismic attribute transforms were applied to three-dimensional (3D) seismic data obtained from “Reigh” Field, Onshore Niger Delta to delineate and characterize geologic features capable of harboring hydrocarbon and identifying hydrocarbon productivity areas within the field. Two (2) sand units were delineated from borehole log data and their corresponding horizons were mapped on seismic data, using appropriate check-shot data of the boreholes. Petrophysical summary of the sand units revealed that the area is characterized by high sand/shale ratio, effective porosity ranged from 16 to 36% and hydrocarbon saturation between 72 and 92%. By extracting attribute maps of coherence, instantaneous frequency, instantaneous amplitude and RMS amplitude, characterization of the sand units in terms of reservoir geomorphological features, facies distribution and hydrocarbon potential was achieved. Seismic attribute results revealed (1) characteristic patterns of varying frequency and amplitude areas, (2) major control of hydrocarbon accumulation being structural, in terms of fault, (3) prospective stratigraphic pinch-out, lenticular thick hydrocarbon sand, mounded sand deposit and barrier bar deposit. Seismic Attributes analysis together with seismic structural interpretation revealed prospective structurally high zones with high sand percentage, moderate thickness and high porosity anomaly at the center of the field. The integration of different seismic attribute transforms and results from the study has improved our understanding of mapped sand units and enhanced the delineation of drillable locations which are not recognized on conventional seismic interpretations.

scholarly journals APPLICATION OF DIRECT HYDROCARBON INDICATORS FOR OIL AND GAS PROSPECTING IN THE DNIPRO-DONETS DEPRESSION

2021 ◽  
pp. 99-108
Author(s):  
Sergiy VYZHVA ◽  
Ihor SOLOVYOV ◽  
Ihor МYKHALEVYCH ◽  
Viktoriia KRUHLYK ◽  
Georgiy LISNY
Keyword(s):  
Acoustic Impedance ◽  
Oil And Gas ◽  
Practical Importance ◽  
Seismic Attributes ◽  
Limited Range ◽  
Seismic Attribute ◽  
Tectonic Model ◽  
Hydrocarbon Traps ◽  
Attribute Analysis ◽  
Volumetric Images

Based on the results of numerous seismic studies carried out in the areas and fields of the Dnipro-Donets depression, the strategy to identify hydrocarbon traps in this region has been developed taking into account modern requirements for prospecting and exploration of gas and oil fields. The studies are designed to determine the favorable zones of hydrocarbon accumulations based on the analysis of the structural-tectonic model. A necessary element for solving such a problem is to aaply direct indicators of hydrocarbons to predict traps of the structural, lithological or combined type. It was determined that an effective approach to identify hydrocarbon traps in the region is attribute analysis employing seismic attributes such as seismic envelope, acoustic impedance or relative acoustic impedance. In most cases of practical importance, the analysis of the distribution of the values of these attributes turned out to be sufficient for performing the geological tasks. It is given an example of extracting additional useful information on the spatial distribution of hydrocarbon traps from volumetric images obtained from seismograms of common sources with a limited range of ray angles inclinations. To analyze the distributions of seismic attribute values, it is recommended to use the Geobody technology for detecting geological bodies as the most effective when using volumetric seismic data. The distributions of various properties of rocks, including zones of increased porosity or zones of presence of hydrocarbons are determined depending on the types of seismic attributes used in the analysis,. The use of several seismic attributes makes it possible to identify geological bodies saturated with hydrocarbons with increased porosity and the like. The paper provides examples of hydrocarbon traps recognition in the areas and fields of the Dnipro-Donets depression practically proved by wells. A generalization on the distribution of promising hydrocarbon areas on the Northern flank of the Dnipro-Donets depression and the relationship of this distribution with the identified structural elements of the geological subsoil is made. 

The Effect of Well Azimuth or Don't Let Your Landman Plan Your Well Path!

2015 ◽  
Author(s):  
Fen Yang ◽  
Larry K. Britt ◽  
Shari Dunn-Norman
Keyword(s):  
Horizontal Well ◽  
Oil And Gas ◽  
Horizontal Wells ◽  
Horizontal Stress ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Principal Stresses ◽  
Lateral Direction ◽  
Well Completion ◽  
Stimulation Of

Abstract Since the late 1980's when Maersk published their work on multiple fracturing of horizontal wells in the Dan Field, the use of transverse multiple fractured horizontal wells has become the completion of choice and become the “industry standard” for unconventional and tight oil and tight gas reservoirs. Today approximately sixty percent of all wells drilled in the United States are drilled horizontally and nearly all of them are multiple fractured. Because a horizontal well adds additional cost and complexity to the drilling, completion, and stimulation of the well we need to fully understand anything that affects the cost and complexity. In other words, we need to understand the affects of the principal stresses, both direction and magnitude, on the drilling completion, and stimulation of these wells. However, little work has been done to address and understand the relationship between the principal stresses and the lateral direction. This paper has as its goal to fundamentally address the question, in what direction should I drill my lateral? Do I drill it in the direction of the maximum horizontal stress (longitudinal) or do I drill it in the direction of the minimum horizontal stress (transverse)? The answer to this question relates directly back to the title of this paper and please "Don't let your land man drive that decision." This paper focuses on the horizontal well's lateral direction (longitudinal or transverse fracture orientation) and how that direction influences productivity, reserves, and economics of horizontal wells. Optimization studies using a single phase fully three dimensional numeric simulator including convergent non-Darcy flow were used to highlight the importance of lateral direction as a function of reservoir permeability. These studies, conducted for both oil and gas, are used to identify the point on the permeability continuum where longitudinal wells outperform transverse wells. The simulations compare and contrast the transverse multiple fractured horizontal well to longitudinal wells based on the number of fractures and stages. Further, the effects of lateral length, fracture half-length, and fracture conductivity were investigated to see how these parameters affected the decision over lateral direction in both oil and gas reservoirs. Additionally, how does completion style affect the lateral direction? That is, how does an open hole completion compare to a cased hole completion and should the type of completion affect the decision on in what direction the lateral should be drilled? These simulation results will be used to discuss the various horizontal well completion and stimulation metrics (rate, recovery, and economics) and how the choice of metrics affects the choice of lateral direction. This paper will also show a series of field case studies to illustrate actual field comparisons in both oil and gas reservoirs of longitudinal versus transverse horizontal wells and tie these field examples and results to the numeric simulation study. This work benefits the petroleum industry by: Establishing well performance and economic based criteria as a function of permeability for drilling longitudinal or transverse horizontal wells,Integrating the reservoir objectives and geomechanic limitations into a horizontal well completion and stimulation strategy,Developing well performance and economic objectives for horizontal well direction (transverse versus longitudinal) and highlighting the incremental benefits of various completion and stimulation strategies.

scholarly journals Porosity Prediction Using Neural Network Based on Seismic Inversion and Seismic Attributes

2019 ◽  
Vol 125 ◽  
pp. 15006
Author(s):  
Taufik Mawardi Sinaga ◽  
M. Syamsu Rosid ◽  
M. Wahdanadi Haidar
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Average Frequency ◽  
Seismic Attributes ◽  
Seismic Inversion ◽  
Carbonate Reservoir ◽  
Neural Network Method ◽  
Time Migration ◽  
The Neural Network ◽  
Network Method

It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

Integrating Advanced Well Completion Technology Improved Well Performance & Maximized Recovery - Case History

2006 ◽  
Author(s):  
A.H. Sunbul ◽  
K.S. Al-Mohanna ◽  
H.B. Al-Qahtani ◽  
D.E. Hembling ◽  
G. Salerno
Keyword(s):  
Case History ◽  
Well Performance ◽  
Well Completion

Genetic‐algorithm/neural‐network approach to seismic attribute selection for well‐log prediction

Geophysics ◽  
2004 ◽  
Vol 69 (1) ◽  
pp. 212-221 ◽  
Author(s):  
Kevin P. Dorrington ◽  
Curtis A. Link
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Reservoir Characterization ◽  
Seismic Attributes ◽  
Optimal Number ◽  
Attribute Selection ◽  
Well Log ◽  
Seismic Attribute ◽  
Neural Network Approach ◽  
Data Set

Neural‐network prediction of well‐log data using seismic attributes is an important reservoir characterization technique because it allows extrapolation of log properties throughout a seismic volume. The strength of neural‐networks in the area of pattern recognition is key in its success for delineating the complex nonlinear relationship between seismic attributes and log properties. We have found that good neural‐network generalization of well‐log properties can be accomplished using a small number of seismic attributes. This study presents a new method for seismic attribute selection using a genetic‐algorithm approach. The genetic algorithm attribute selection uses neural‐network training results to choose the optimal number and type of seismic attributes for porosity prediction. We apply the genetic‐algorithm attribute‐selection method to the C38 reservoir in the Stratton field 3D seismic data set. Eleven wells with porosity logs are used to train a neural network using genetic‐algorithm selected‐attribute combinations. A histogram of 50 genetic‐algorithm attribute selection runs indicates that amplitude‐based attributes are the best porosity predictors for this data set. On average, the genetic algorithm selected four attributes for optimal porosity log prediction, although the number of attributes chosen ranged from one to nine. A predicted porosity volume was generated using the best genetic‐algorithm attribute combination based on an average cross‐validation correlation coefficient. This volume suggested a network of channel sands within the C38 reservoir.

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