Integration of Cores-Well log-Seismic Data for Estimating Rock Properties and Sand Distributions. A Case Study from a Gas Field in Sabah

2014 ◽  
Author(s):  
B.P. Kantaatmadja ◽  
A. Nurhono ◽  
M. Othman ◽  
M.N. A. Rahman
Keyword(s):  
Seismic Data ◽  
Rock Properties ◽  
Well Log ◽  
Gas Field

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.

THE INTEGRATED USE OF PETROPHYSICAL, AVO, SEISMIC INVERSION AND 3D VISUALISATION TOOLS FOR RESERVOIR CHARACTERISATION

2001 ◽  
Vol 41 (2) ◽  
pp. 131
Author(s):  
A.G. Sena ◽  
T.M. Smith
Keyword(s):  
Seismic Data ◽  
New Technology ◽  
Seismic Inversion ◽  
Well Log ◽  
Reservoir Properties ◽  
Gas Field ◽  
Geological Interpretation ◽  
3D Visualisation ◽  
Quantitative Understanding ◽  
Inversion Techniques

The successful exploration for new reservoirs in mature areas, as well as the optimal development of existing fields, requires the integration of unconventional geological and geophysical techniques. In particular, the calibration of 3D seismic data to well log information is crucial to obtain a quantitative understanding of reservoir properties. The advent of new technology for prestack seismic data analysis and 3D visualisation has resulted in improved fluid and lithology predictions prior to expensive drilling. Increased reservoir resolution has been achieved by combining seismic inversion with AVO analysis to minimise exploration risk.In this paper we present an integrated and systematic approach to prospect evaluation in an oil/gas field. We will show how petrophysical analysis of well log data can be used as a feasibility tool to determine the fluid and lithology discrimination capabilities of AVO and inversion techniques. Then, a description of effective AVO and prestack inversion tools for reservoir property quantification will be discussed. Finally, the incorporation of the geological interpretation and the use of 3D visualisation will be presented as a key integration tool for the discovery of new plays.

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.

Creating detailed subsurface models using predictive image-guided well-log interpolation

2017 ◽  
Vol 5 (3) ◽  
pp. T279-T285 ◽  
Author(s):  
Parvaneh Karimi ◽  
Sergey Fomel ◽  
Rui Zhang
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Structural Information ◽  
Rock Properties ◽  
Well Log ◽  
Initial Model ◽  
The North ◽  
Structural Framework ◽  
Seismic Image ◽  
Seismic Reflectors

Integration of well-log data and seismic data to predict rock properties is an essential but challenging task in reservoir characterization. The standard methods commonly used to create subsurface model do not fully honor the importance of seismic reflectors and detailed structural information in guiding the spatial distribution of rock properties in the presence of complex structures, which can make these methods inaccurate. To overcome initial model accuracy limitations in structurally complex regimes, we have developed a method that uses the seismic image structures to accurately constrain the interpolation of well properties between well locations. A geologically consistent framework provides a more robust initial model that, when inverted with seismic data, delivers a highly detailed yet accurate subsurface model. An application to field data from the North Sea demonstrates the effectiveness of our method, which proves that incorporating the seismic structural framework when interpolating rock properties between wells culminates in the increased accuracy of the final inverted result compared with the standard inversion workflows.

Seismic-petrophysical reservoir characterization in the northern part of the Chicontepec Basin, Mexico

2016 ◽  
Vol 4 (3) ◽  
pp. T403-T417 ◽  
Author(s):  
Supratik Sarkar ◽  
Sumit Verma ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Rock Properties ◽  
Well Log ◽  
Third Order ◽  
East Central ◽  
Well Control ◽  
Seismic Geomorphology ◽  
Tight Reservoirs

The Chicontepec Formation in east-central Mexico is comprised of complex unconventional reservoirs consisting of low-permeability disconnected turbidite reservoir facies. Hydraulic fracturing increases permeability and joins these otherwise tight reservoirs. We use a recently acquired 3D seismic survey and well control to divide the Chicontepec reservoir interval in the northern part of the basin into five stratigraphic units, equivalent to global third-order seismic sequences. By combining well-log and core information with principles of seismic geomorphology, we are able to map deepwater facies within these stratigraphic units that resulted from the complex interaction of flows from different directions. Correlating these stratigraphic units to producing and nonproducing wells provides the link between rock properties and Chicontepec reservoirs that could be delineated from surface seismic data. The final product is a prestack inversion-driven map of stacked pay that correlates to currently producing wells and indicates potential untapped targets.

Using seismic inversion and net pay to calibrate Eagle Ford Shale producible resources

2015 ◽  
Vol 3 (3) ◽  
pp. SV69-SV78
Author(s):  
Bo Chen ◽  
Dhananjay Kumar ◽  
Anthony Uerling ◽  
Sheryl Land ◽  
Omar Aguirre ◽  
...  
Keyword(s):  
Seismic Data ◽  
Strong Correlation ◽  
Estimation Method ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Production Volume ◽  
Well Log ◽  
Engineering Analysis ◽  
Eagle Ford Shale ◽  
Eagle Ford

We found a strong correlation between the estimated production volume and hydrocarbon resources in thicker and more porous intervals in the Eagle Ford Shale through integrated petrophysical and engineering analysis. The wells analyzed were selected with similar operational designs so that the rock properties were the main variables impacting the production volume. Seismic data were used to characterize such desired rock properties, including thickness and porosity, to evaluate the producing potentials across the field. Seismic interpretation provided the top and base of the Eagle Ford reservoir, and hence, its thickness. Seismic inversion calibrated the acoustic impedance. Also, the seismic net pay estimation method predicted the thickness of the more porous intervals. The calculated seismic net pay agreed with the well log data. As petrophysical analysis suggested, the seismic net pay also formed a strong correlation with the production volume and has been used to predict the producible resources for new wells, identify refract candidates, and evaluate completion trial methods in the Eagle Ford Shale.

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