scholarly journals U.S. Geological Survey input-data forms for the assessment of the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017

2017 ◽  
Author(s):  
Kristen R. Marra
Keyword(s):  
Input Data ◽  
Geological Survey ◽  
Permian Basin ◽  
Midland Basin

Assessment of undiscovered continuous oil resources in the Wolfcamp shale of the Midland Basin, Permian Basin Province, Texas, 2016

Fact Sheet ◽  
2016 ◽  
Author(s):  
Stephanie B. Gaswirth ◽  
Kristen R. Marra ◽  
Paul G. Lillis ◽  
Tracey J. Mercier ◽  
Heidi M. Leathers-Miller ◽  
...  
Keyword(s):  
Permian Basin ◽  
Midland Basin ◽  
Oil Resources

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 Assessment of undiscovered oil and gas resources in the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017

Fact Sheet ◽  
2017 ◽  
Author(s):  
Kristen R. Marra ◽  
Stephanie B. Gaswirth ◽  
Christopher J. Schenk ◽  
Heidi M. Leathers-Miller ◽  
Timothy R. Klett ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Permian Basin ◽  
Midland Basin ◽  
Oil And Gas Resources

Fabric and internal architecture of Permian Basin turbidites indicated by unsupervised machine learning analysis of P-P and SV-P images

2020 ◽  
Vol 8 (4) ◽  
pp. SV1-SV15
Author(s):  
Bob Hardage ◽  
Tom Smith ◽  
Diana Sava ◽  
Yi Wang ◽  
Rocky Roden ◽  
...  
Keyword(s):  
Machine Learning ◽  
New Technologies ◽  
The United States ◽  
S Wave ◽  
Permian Basin ◽  
Unsupervised Machine Learning ◽  
Midland Basin ◽  
Delaware Basin ◽  
Internal Architecture ◽  
P Sources

The Permian Basin of west Texas spans two major subbasins — the Midland Basin and the Delaware Basin. Both basins contain Wolfcampian- to Leonardian-age turbidites that form thick sections of prolific unconventional reservoirs. For the past several years, the most active drilling targets in the United States have been the Wolfberry turbidite interval of the Midland Basin and the Wolfbone turbidite interval in the Delaware Basin. We have used two new technologies to examine the internal architecture and fabric of a thick interval of these unconventional drilling targets with seismic reflection seismology. First, we used seismic interpretation software that uses unsupervised machine learning (ML), so that a higher level of detail could be extracted from seismic images. Second, we complemented our P-P imaging of Wolfberry turbidites with a new seismic imaging option, that being SV-P (or converted-P) imaging. Because vertical vibrators, particularly arrays of vertical vibrators, produce downgoing P and downgoing SV illuminating wavefields, SV-P reflections can usually be extracted from the same vertical-geophone responses as are P-P reflections. The combination of these two images essentially doubles the amount of information that can be extracted from data generated by P sources and recorded with vertical geophones. SV-P imaging with P sources has been ignored by reflection seismologists for decades, so we felt an obligation to illustrate the value of this ignored seismic mode. These two new tools — SV-P imaging and interpreting P-P and SV-P images via unsupervised ML — expanded our insights into the internal architecture and fabric of Wolfberry turbidites. Our work provides interpreters a much-needed example of applying unsupervised ML technology in a joint interpretation of P- and S-wave data.

scholarly journals U.S. Geological Survey input-data forms for the assessment of the Upper Jurassic Haynesville Formation, U.S. Gulf Coast, 2016

2018 ◽  
Author(s):  
Stanley T. Paxton ◽  
Janet K. Pitman ◽  
Scott A. Kinney ◽  
Nicholas J. Gianoutsos ◽  
Ofori N. Pearson ◽  
...  
Keyword(s):  
Input Data ◽  
Gulf Coast ◽  
Upper Jurassic ◽  
Geological Survey ◽  
Haynesville Formation

scholarly journals U.S. Geological Survey input-data forms for the assessment of the Upper Jurassic Bossier Formation, U.S. Gulf Coast, 2016

2018 ◽  
Author(s):  
Stanley T. Paxton ◽  
Janet K. Pitman ◽  
Scott A. Kinney ◽  
Nicholas J. Gianoutsos ◽  
Ofori N. Pearson ◽  
...  
Keyword(s):  
Input Data ◽  
Gulf Coast ◽  
Upper Jurassic ◽  
Geological Survey

Data-Mining Approach Evaluates Production Performance in the Permian Basin

2021 ◽  
Vol 73 (01) ◽  
pp. 46-47
Author(s):  
Chris Carpenter
Keyword(s):  
Data Mining ◽  
Horizontal Wells ◽  
Production Performance ◽  
Asia Pacific ◽  
Production Data ◽  
Permian Basin ◽  
Midland Basin ◽  
Data Mining Approach ◽  
Cumulative Production ◽  
Short And Long Term

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 198192, “Production Performance Evaluation From Stimulation and Completion Parameters in the Permian Basin: Data-Mining Approach,” by Mustafa A. Al-Alwani, SPE, and Shari Dunn-Norman, SPE, Missouri University of Science and Technology, and Larry K. Britt, SPE, NSI Fracturing, et al., prepared for the 2019 SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, 18–19 November, Brisbane, Australia. The paper has not been peer reviewed. The complete paper uses 3,782 unconventional horizontal wells to analyze the effect of proppant volume and the length of the perforated lateral on short- and long-term well productivity across the Permian Basin. Tying cumulative production to completion and stimulation practices showed that increasing pumped proppant per well from 5 million to less than 10 million lbm yielded a 34% increase in 5-year cumulative average barrels of oil equivalent (BOE). Raising the pumped proppant per well to 10 million-15 million lbm and 15 million-20 million lbm increased 5-year cumulative BOE from the previous proppant range group to 27% and 18.5%, respectively. Introduction For this study, stimulation chemical data from Permian (Midland) Basin wells were downloaded from FracFocus for all horizontal wells completed and stimulated between 2012 and 2018. The data were then subjected to rigorous cleaning and processing, a process detailed in the complete paper, and then combined with DrillingInfo completion and production parameters. Combining these data provided ample parameters for stimulation, completion, and production data. The objective of the study was to investigate the production performance of Permian Basin wells as a result of different ranges of stimulation and completion parameters. Fig. 1 shows a database representation of the major counties in the Permian Basin with the number of wells in each county. Results and Discussion To substitute for any quantities of produced gas, all production data have been converted to BOE by using the conversion factor of 1 BOE=6 Mcf. The amount of proppant being pumped and the length of the perforated lateral length have been selected to represent the stimulation size and the completion magnitude, respectively.

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