Synthesis of capillary pressure curves from post-stack seismic data with the use of intelligent estimators: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin

2015 ◽  
Vol 112 ◽  
pp. 215-225 ◽  
Author(s):  
Naser Golsanami ◽  
Ali Kadkhodaie-Ilkhchi ◽  
Amir Erfani
Keyword(s):  
Capillary Pressure ◽  
Seismic Data ◽  
Persian Gulf ◽  
The South ◽  
Gas Field ◽  
Capillary Pressure Curves

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

Estimation and mapping of vitrinite reflectance from seismic data in South Pars gas field, Persian Gulf

2019 ◽  
Vol 164 ◽  
pp. 1-10 ◽  
Author(s):  
Mohammad Ali Faraji ◽  
Ali Kadkhodaie ◽  
David A. Wood ◽  
Hossain Rahimpour-Bonab ◽  
Mandana Ghanavati
Keyword(s):  
Seismic Data ◽  
Persian Gulf ◽  
Vitrinite Reflectance ◽  
Gas Field

Evaluation of direct hydrocarbon indicators through comparison of compressional‐ and shear‐wave seismic data: a case study of the Myrnam gas field, Alberta

Geophysics ◽  
1985 ◽  
Vol 50 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Ross Alan Ensley
Keyword(s):  
Shear Wave ◽  
Seismic Data ◽  
Compressional Wave ◽  
Bright Spot ◽  
Low Velocity ◽  
Gas Reservoir ◽  
Gas Field ◽  
Compressional Waves ◽  
The Relationship

Shear waves differ from compressional waves in that their velocity is not significantly affected by changes in the fluid content of a rock. Because of this relationship, a gas‐related compressional‐wave “bright spot” or direct hydrocarbon indicator will have no comparable shear‐wave anomaly. In contrast, a lithology‐related compressional‐wave anomaly will have a corresponding shear‐wave anomaly. Thus, it is possible to use shear‐wave seismic data to evaluate compressional‐wave direct hydrocarbon indicators. This case study presents data from Myrnam, Alberta which exhibit the relationship between compressional‐ and shear‐wave seismic data over a gas reservoir and a low‐velocity coal.

Sign in / Sign up

Export Citation Format

Share Document