Oil and Gas
Oil and Gas | Geophysics and Seismic Interpretation
Quantitative Reservoir Characterization
Format and Duration:
5 days
10 sessions
This course provides participants with the skills to design and execute workflows to achieve optimum seismic reservoir characterisation results. The course focusses on methodologies to enhance the seismic data to make quantitative estimates of reservoir properties and includes a comprehensive review of AVO and AVA methods.
From quantitative analysis of pre-stack seismic data, elastic properties of the reservoir can be derived and translated into relevant rock properties such as porosity and fluid saturations. The use of gravity, magnetics, electrical, electromagnetics and spectral data to complement seismic methods in subsurface evaluation provides ways of reducing uncertainty of subsurface models.
Machine Learning methods are included which provide means of classification and clustering of seismic reservoirs, using open-source software like Weka, Keras and TensorFlow. A working knowledge of the seismic method is assumed.
Schedule
Duration and Training Method
The course is available as a classroom or virtual classroom format. It uses a mixture of lectures, practical exercises and direct (workshop-like) participant involvement, complemented with case histories.
The course can be customized to meet specific needs of participants.
Course Overview
Learning Outcomes
Participants will learn to:
- Develop a solid foundation in and conceptual understanding of Seismic Quantitative Interpretation.
- Construct coherent workflows to estimate reservoir properties and associated uncertainties by integrating seismic data with other data types.
- Apply appropriate methodologies of seismic data processing (e.g. AVO and AVA) to determine reservoir properties.
- Use quantitative analysis of pre-stack seismic data to derive elastic and translate into relevant rock properties such as porosity and fluid saturations.
- Use gravity, magnetics, electrical, electromagnetics and spectral data to complement seismic methods in subsurface evaluation.
- Use Machine Learning applications for the classification and clustering of reservoir characteristics.
Course Content
Part 1
- Geophysical Methods, Seismic Acquisition and Processing, Workflow,
- Seismic for QI
- Rock Physics
- Seismic Resolution: Point-Spread or Resolution Functions
Part 2
- Structural & Stratigraphic Interpretation, Tuning: Simmons & Backus
- Tuning Wedge, Tuning amplitude variation with angle of incidence (AVA)
- Fluid Replacement, amplitude variation with offset (AVO)
- Anisotropy, Amplitude variation with angle of incidence (AVA)
Part 3
- Amplitude variation with angle of incidence (AVA) (ΔRPP, ΔRSS)
- Inhomogeneity & Anisotropy
- Wave Equation amplitude variation with offset (AVO), Activation Functions
- Amplitude variation with angle of incidence (AVA) vertical and horizontal transverse anisotropy
Part 4
- AVAz Fractures, Machine learning,
- Fractures Amplitude variation with angle of incidence (AVA)
- Machine Learning amplitude variation with offset (AVO), Inversion
- Machine Learning Classification
- Classification, Inversion vs Machine learning I, Clustering
- Machine Learning Amplitude variation with angle of incidence (AVA)
- Clustering
- Supervised, Unsupervised and Semi-Supervised learning
Part 5
- Hydrocarbon Indicators,
- Gassmann Fluid Replacement
- Machine Learning: Keras, TensorFlow
- Machine Learning Regression
- Gassmann subsalt rock
Who Should Attend and Prerequisites
Geologists, geophysicists, petroleum and reservoir engineers, involved in exploration and development of hydrocarbon fields.
Instructors
Jaap C. Mondt
Background
Jaap Mondt studied geology at the University of Leiden and after his Bachelors went to Utrecht University to study geophysics.
After his PhD in Utrecht on "Full wave theory and the structure of the lower mantle", Jaap joined Shell in The Hague to work on the prediction of lithology and pore-fluid based on seismic, petrophysical and geological input. During his subsequent 3-year stay with Shell Expro in London Jaap interpreted seismic data from the Central North Sea area. He was also actively involved in seismic experiments from drilling rigs and production platforms.
After his return to The Netherlands Jaap headed a team on the development of 3D interpretation methods using workstations. After a period of Quality Assurance of "Contractor" software for seismic processing, Jaap became responsible for Geophysics in the Shell Learning Centre. At the same time he was a part-time professor in Applied Geophysics at the University of Utrecht.
From 2001 till 2005 Jaap worked on the use of Potential Field Methods (Gravity, Magnetic fields) for detecting oil and gas, later extended with the use of electromagnetic fields. After his retirement from Shell he founded his own company (Breakaway), providing courses and advice on the acquisition and processing of geophysical data (seismic, gravity, magnetic and electromagnetic data). In recent years Jaap has specialised in the development of Blended Learning.
Courses Taught
N385: Workflows for Seismic Reservoir Characterisation
Accreditation and Certification
CEU: 3.5 Continuing Education Units
PDH: 35 Professional Development Hours
Certificate: Certificate Issued Upon Completion
RPS is accredited by the International Association for Continuing Education and Training (IACET) and is authorized to issue the IACET CEU. We comply with the ANSI/IACET Standard, which is recognised internationally as a standard of excellence in instructional practices.
We issue a Certificate of Attendance which verifies the number of training hours attended. Our courses are generally accepted by most professional licensing boards/associations towards continuing education credits. Please check with your licensing board to determine if the courses and certificate of attendance meet their specific criteria.