Prospect Maturation: Seismic Interpretation For Hydrocarbon

Seismic Interpretation for Hydrocarbon prospect maturation, Risk analysis, Attribute, AVO, DHI for Oil & Gas Exploration

What you'll learn

Understand seismic interpretation and apply it for prospect identification and maturation in hydrocarbon exploration.

Evaluate exploration risks, success probability, and basic exploration economics for informed decision-making.

Learn and be able to implement the Geophysical attributes, including AVO, DHI, Spectral, Geometric, and many more.

Grasp machine learning basics and explore its applications in seismic data analysis and geoscience workflows.

Requirements

Familiarity with seismic data formats and exploration concepts is beneficial.

A basic understanding of geology or geophysics is helpful but not required.

No prerequisite course is necessary to proceed with this course. You will have the opportunity to learn the concepts from basic to advanced levels, enhancing your skills effectively.

A laptop or PC with internet access for viewing course content and downloading materials.

No prior experience with machine learning or coding is needed.

Description

This course offers a comprehensive journey through the science of seismic interpretation, focusing on hydrocarbon prospect maturation using advanced geophysical techniques and the power of machine learning. Designed for geoscience students, researchers, early-career professionals, and exploration geophysicists or geoscientists, this course equips learners with both foundational knowledge and cutting-edge tools used in the oil and gas industry today.You will start by understanding the seismic interpretation workflow—from identifying structural and stratigraphic traps to evaluating their prospectivity. The course then dives into key geophysical attributes such as Amplitude Variation with Offset (AVO), Direct Hydrocarbon Indicators (DHIs), spectral decomposition, geometric attributes, and many more related methods. These tools are essential for characterizing reservoirs, identifying bypassed pay zones, and reducing exploration risk.A unique aspect of this course is its practical and introduction to machine learning applications in geoscience. You’ll explore how ML can be used for seismic facies classification, pattern recognition, and predictive modeling to enhance prospect evaluation.Whether you are aiming to support exploration decisions, reduce risk, or simply broaden your technical skills, this course provides the skills and insights needed to advance your career in the energy sector. No coding experience is required—just curiosity and a background in geology or geophysics.

Overview

Section 1: Prospects, Its Maturation, and Plays

Lecture 1 Prospects, Its Maturation, and Plays

Lecture 2 What are the Prospect Stages?

Lecture 3 Which Prospect to Drill? Prospect Analysis

Lecture 4 Looking at the Bigger Picture: The Focus of Oil & Gas Industry

Lecture 5 An Exploration Success : Case Study

Lecture 6 Exercise # 1 : Define prospect elements

Lecture 7 Exercise # 2 : Estimating trap volumes

Lecture 8 Exercise # 3 : Hydrocarbon Type

Lecture 9 Exercise # 4 : Assessment Deterministic & Probabilistic

Lecture 10 Exercise # 5 : Risk (POS or COS) | How Confident Are We?

Lecture 11 Final Results : a) Analysis on Alpha & Beta Prospect

Lecture 12 Introduction to Prospects and portfolios

Section 2: Understanding Seismic as a Geological Interpretation

Lecture 13 Seismic Response : Snell’s Law

Lecture 14 Seismic Response: Acoustic impedance & Reflection coefficient

Lecture 15 Seismic Polarity and Phase

Lecture 16 Seismic interpretation in Prospect Maturation and its objectives

Lecture 17 Seismic Interpretation Workflow

Lecture 18 Development Planning for Field/Block

Lecture 19 Production Planning for Field/Block

Lecture 20 Life of field: Exploration > Appraisal > Development > Production

Section 3: Machine Learning Overview and Applications

Lecture 21 Machine Learning : An overview

Lecture 22 Why Machine Learning?

Lecture 23 How Machine Learning?

Lecture 24 Machine learning in Seismic Exploration Workflow and Examples

Lecture 25 Types of Machine Learning

Lecture 26 1- Supervised Machine Learning?

Lecture 27 2- Unsupervised Machine Learning?

Lecture 28 Unsupervised Machine Learning In Geophysics

Lecture 29 3- Semi-Supervised Machine Learning?

Lecture 30 4- Reinforcement Machine Learning?

Lecture 31 Case Studies: ML: Seismic Interpretation with (SOM)

Lecture 32 AI, ML, DL, Gen AI

Lecture 33 Essential Tools for Prospect Maturation and Reservoir Modeling

Section 4: Petroleum System

Lecture 34 Introduction to Petroleum System

Lecture 35 1- Source Rock : Where is Oil and Where is Gas

Lecture 36 2- Reservoir Rock and its types

Lecture 37 3- Seal Or Cap Rock

Lecture 38 4- Traps Important HC Provinces

Lecture 39 5- Hydrocarbon Migration

Lecture 40 Worldwide Important HC Provinces:

Section 5: Seismic Stratigraphy and Machine Learning Applications

Lecture 41 Introduction to Seismic Stratigraphy

Lecture 42 Types of Stratigraphy Reflection

Lecture 43 Sedimentary Reflections : 1- Unconformities

Lecture 44 Sedimentary Reflections : 2- Seismic facies units

Lecture 45 Sedimentary Reflections : 3- Sequence Stratigraphy

Lecture 46 Sedimentary Reflections: 4- System Tracts

Lecture 47 Sedimentary Reflections : 5- Technical Errors

Lecture 48 Sedimentary Reflections: 6- Seismic Stratigraphy Workflow

Lecture 49 Machine Learning : Mapping Stratigraphic Traps

Lecture 50 Machine Learning : Mineralogical Composition in Unconventional Formations

Section 6: Seismic Attributes

Lecture 51 Introduction to Seismic Attribute

Lecture 52 Why and What are the Uses of Seismic Attribute

Lecture 53 Classifications of Seismic Attribute

Lecture 54 Applications and Choosing right Attribute

Lecture 55 Complex Trace Attributes: Envelope, Instantaneous Phase & Frequency

Lecture 56 Geometric Attributes: Dip & Azimuth, Coherency, Curvature, Sweetness

Lecture 57 Case Study : High-Resolution Discontinuity Attributes

Section 7: Spectral Analysis & AVO

Lecture 58 Introduction to Spectral Decomposition

Lecture 59 Workflow and Applications of Spectral Decomposition

Lecture 60 Case Study-1: Spectral Decomposition With Multi-Attributes

Lecture 61 Case Study-2: Spectral Decomposition With Multi-Attributes

Lecture 62 Key Takeaway for Spectral Decomposition

Lecture 63 Amplitude Versus Offset (AVO), & Its Importance

Lecture 64 AVO Classes

Lecture 65 AVO As a Sand Indicator

Lecture 66 AVO as a Fluid Indicator

Lecture 67 Case Study: Clastic and Non-Clastic Reservoir

Lecture 68 Direct Hydrocarbon Indicator (DHI)

Lecture 69 Motivation for improving interpretation: Key Messages

Section 8: Hands-On Seismic Interpretation and Prospect Maturation on Software

Lecture 70 Introduction to Interpretation Software and Requirements

Lecture 71 Let’s Strat Together: Data Loading & Visualization

Lecture 72 Sub-Volume, Time and/or Horizon Extraction

Lecture 73 Well to Seismic Tie/Correlation

Lecture 74 Available Seismic Attributes In the Software

Lecture 75 Geological Model using a Cost Function Minimization Algorithm

Lecture 76 Recheck and Enhance your Interpretation Skills

Lecture 77 Geo-Model From Model-Grid

Lecture 78 Horizon Stack

Lecture 79 Automatic Fault Extraction (AFE)

Lecture 80 Geo-Body & Gross Rock Volume (GRV)

Lecture 81 Calculator For Volumes, Geo-Models, Horizons, Logs, and 2D Lines

Lecture 82 Prospect # 1 : Shallow Gas and Volume Assessments

Lecture 83 Prospect # 2 : Coaly Sandstones above the Zechstein salt: Volume Assessments

Section 9: Exploration Economic - NPV, NCF, EMV, STOIIP, HCIIP

Lecture 84 Exploration Economics

Lecture 85 Net Present Value (NPV) & Net Cash Flow (NCF)

Lecture 86 Expected Monetary Value (EMV)

Lecture 87 Cost Calculation: Seismic, Exploration, Development, and Efficiency

Lecture 88 Stock tank oil-initially-in-place   (STOIIP)

Lecture 89 Hydrocarbons Initially In Place (HCIIP)

Lecture 90 Exercise # 1: HCIIP

Lecture 91 Remarks on Exploration Economics

Geoscience students (undergraduate to postgraduate) seeking to build a strong foundation in seismic interpretation and geophysical attributes.,Early-career geophysicists and geologists working in oil and gas exploration who want to enhance their technical skills in prospect evaluation and risk assessment.,Exploration professionals aiming to integrate machine learning into their seismic interpretation workflows.,Academics and researchers interested in the practical applications of AVO, DHI, and spectral attributes in subsurface analysis.,Engineers and data scientists entering the geoscience field who want a structured introduction to geophysical data interpretation and prospecting.