Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models ebook

Table of Contents

Cover

Title Page

Foreword

Foreword

WHAT ANALYTICS IS NOT

WHAT ANALYTICS ARE

Preface

Acknowledgments

CHAPTER 1: Introduction to Data‐Driven Concepts

INTRODUCTION

A DATA‐DRIVEN STUDY TIMELINE

IS IT INDUCTION OR DEDUCTION?

REFERENCES

CHAPTER 2: Data‐Driven Analytical Methods Used in E&P

INTRODUCTION

SOFT COMPUTING TECHNIQUES

RULES‐BASED METHODS

ARTIFICIAL INTELLIGENCE: MACHINE AND DEEP LEARNING

REFERENCES

CHAPTER 3: Advanced Geophysical and Petrophysical Methodologies

INTRODUCTION

ADVANCED GEOPHYSICAL METHODOLOGIES

ADVANCED PETROPHYSICAL METHODOLOGIES

REFERENCES

CHAPTER 4: Continuous Monitoring

INTRODUCTION

CONTINUOUS MONITORING IN THE RESERVOIR

MACHINE LEARNING TECHNIQUES FOR TEMPORAL DATA

SPATIOTEMPORAL PERSPECTIVES

TIME SERIES ANALYSIS

ADVANCED TIME SERIES PREDICTION

DIGITAL SIGNAL PROCESSING THEORY

HYDRAULIC FRACTURE MONITORING AND MAPPING

COMPLETIONS EVALUATION

RESERVOIR MONITORING: REAL‐TIME DATA QUALITY

DISTRIBUTED ACOUSTIC SENSING

DISTRIBUTED TEMPERATURE SENSING

CASE STUDY: TIME SERIES TO OPTIMIZE HYDRAULIC FRACTURE STRATEGY

REFERENCES

CHAPTER 5: Seismic Reservoir Characterization

INTRODUCTION

SEISMIC RESERVOIR CHARACTERIZATION: KEY PARAMETERS

MODULAR ARTIFICIAL NEURAL NETWORKS

WAVELET ANALYSIS

SPECTRAL DECOMPOSITION

FIRST ARRIVALS

NOISE SUPPRESSION

REFERENCES

CHAPTER 6: Seismic Attribute Analysis

INTRODUCTION

TYPES OF SEISMIC ATTRIBUTES

SEISMIC ATTRIBUTE WORKFLOWS

SEISMIC FACIES CLASSIFICATION

REFERENCES

CHAPTER 7: Geostatistics: Integrating Seismic and Petrophysical Data

INTRODUCTION

THE COVARIANCE AND THE VARIOGRAM

CASE STUDY: SPATIALLY PREDICTED MODEL OF ANISOTROPIC PERMEABILITY

KRIGING AND CO‐KRIGING

GEOSTATISTICAL INVERSION

KNOWLEDGE SYNTHESIS: BAYESIAN MAXIMUM ENTROPY (BME)

REFERENCES

CHAPTER 8: Artificial Intelligence: Machine and Deep Learning

INTRODUCTION

DATA MANAGEMENT

MACHINE LEARNING METHODOLOGIES

DEEP LEARNING TECHNIQUES

DEEP NEURAL NETWORK ARCHITECTURES

SEISMIC FEATURE IDENTIFICATION WORKFLOW

REFERENCES

CHAPTER 9: Case Studies: Deep Learning in E&P

INTRODUCTION

RESERVOIR CHARACTERIZATION

CASE STUDY: SEISMIC PROFILE ANALYSIS

CASE STUDY: ESTIMATED ULTIMATE RECOVERY

CASE STUDY: DEEP LEARNING APPLIED TO WELL DATA

CASE STUDY: GEOPHYSICAL FEATURE EXTRACTION: DEEP NEURAL NETWORKS

CASE STUDY: WELL LOG DATA‐DRIVEN EVALUATION FOR PETROPHYSICAL INSIGHTS

CASE STUDY: FUNCTIONAL DATA ANALYSIS IN RESERVOIR MANAGEMENT

REFERENCES

Glossary

About the Authors

Index

End User License Agreement

List of Tables

Chapter 06

Table 6.1 Curvature Attributes

Table 6.2 Rock Solid Attributes

Table 6.3 Seismic Facies Classification Measurements

Table 6.4 Statistical Comparison between the Different Data‐Driven Methods

Chapter 07

Table 7.1 Pairwise Distance Intervals Tabulated for Each Lag Class

Table 7.2 Parameter Values for the Fitted Gaussian‐Gaussian Model

Table 7.3 Parameter Values for the Fitted Exponential Model

Chapter 09

Table 9.1 Input Data for EUR Study

List of Illustrations

Chapter 02

Figure 2.1 Multidisciplinary nature of soft computing technologies

Figure 2.2 Analytical workflow demonstrating an ensemble model

Figure 2.3 Simplest expression of a neural network

Figure 2.4 Perceptron architecture for a neural network

Figure 2.5 Hidden layer within the neural network architecture

Figure 2.6 Gradient descent solves linear regression

Chapter 03

Figure 3.1 A typical spectral window of 4500 samples from a single component channel of passive seismic data lasting 9 seconds from a sea‐floor seismic array. The signal to noise ratio is about 1:1. Dominant frequencies include 60 Hz mains AC and its harmonics, and much lower frequencies from nearby drilling.

Figure 3.2 Petrophysical business studies in a data science context

Figure 3.3 A statistical study of well log data indicates that the elastic impedance at the formation level across 180 wells falls into two clusters. Closer inspection of the data revealed that a milli‐ prefix had persisted for some wells. This is a data governance issue to be addressed before data can be used for analysis.

Figure 3.4 Average formation density and its standard deviation for the BRENT group (Broom blue/circle, Rannoch pink/X, Etive brown/square, Ness purple/+, Tarbert red/*). The more heterogeneous Etive formation contrasts with the homogeneous sandstone of the Broom. Of interest are the outlier wells for each group and the diversity in variability across the Tarbert.

Figure 3.5 Coal measures determined in a borehole in the Taranaki Basin, NE New Zealand, were used to create a wavelet‐based template that was applied to the boreholes for the rest of the basin with great success. Rahman (2015) reports occasional false positives pointing to a need to tune the technique further, but also reports many candidate features that had not been interpreted as coal but occurred in interpreted formations that were associated with coal.

Chapter 04

Figure 4.1 Original time series convolved additive model

Figure 4.2 Decomposition panels illustrating trends and seasonal cycles

Figure 4.3 Time series patterns enable a hydrocarbon production gap analysis

Figure 4.4 Gap analysis for a typical well

Figure 4.5 Data QC workflows

Figure 4.6 Data QC solution platform

Figure 4.7 Surface plot of temperature DTS data

Figure 4.8 Surface plot illustrating temperature gradients across the entire fractured wellbore

Figure 4.9 Sankey diagram for the cumulative gas production

Figure 4.10 Correlation matrix

Figure 4.11 Bubble plot

Figure 4.12 Network diagram

Figure 4.13 SOM diagram—Qg100

Figure 4.14 SOM diagram—Bulk modulus

Figure 4.15 SOM diagram—Fracture stages

Figure 4.16 SOM diagram—Proppant volume

Chapter 05

Figure 5.1 Multivariate, multivariant, multidimensional, and stochastic seismic characteristics

Figure 5.2 Traditional seismic fault interpretation

Figure 5.3 Seismic reservoir characterization data‐driven methodology

Figure 5.4 Principal component analysis workflow

Figure 5.5 Line plot showing seismic trace data post F‐K transform

Figure 5.6 Summary of the decomposition analysis illustrating the wavelet coefficients

Figure 5.7 Detail coefficient plot for all levels with independent scaling

Figure 5.8 Detail coefficient plot with the top three levels scaled uniformly

Figure 5.9 Detail coefficient plot using the Donoho and Johnstone algorithm

Figure 5.10 Multiresolution approximation plot showing restorations of the input signal

Figure 5.11 Multiresolution approximation plot of a particular level

Figure 5.12 Multiresolution approximation plot for level 10

Figure 5.13 Multiresolution decomposition plot for the seismic data

Figure 5.14 Wavelet scalogram of the seismic trace

Figure 5.15 Wavelet scalogram showing most energy at level 8

Figure 5.16 Line plot showing seismic trace data post wavelet smoothing

Figure 5.17 True surface

g

(

t, x

)

Figure 5.18 Noisy observations

Z

(

t, x

)

Figure 5.19 Estimated smooth surface

Figure 5.20 Shot record with ground roll before applying noise suppression algorithm

Figure 5.21 Shot record after applying noise suppression algorithm

Figure 5.22 The attenuated noise

Chapter 06

Figure 6.1 SEMMA process for data‐driven analysis

Figure 6.2 Classification in a seismic facies interpretation iterative cycle

Figure 6.3 Seismic facies classification workflow

Figure 6.4 Dendrogram generated by a hierarchical clustering algorithm

Figure 6.5 Constellation plot

Figure 6.6

k

‐means cluster visualization

Figure 6.7 Clusters depicted with contours for the normal densities

Figure 6.8 Ternary plot explaining the cluster probabilities for each seismic observation

Figure 6.9 Principal components on correlations report

Figure 6.10 Seismic classification set of clusters from a business perspective

Chapter 07

Figure 7.1 Geostatistics and earth modeling

Figure 7.2 Basic assumption of geostatistics

Figure 7.3 Trend surface analysis

Figure 7.4 Scatter plot of permeability observations at discrete locations across the reservoir

Figure 7.5 Histogram detailing lag classes and the trends in the measurement

Figure 7.6 Scatter plot of permeability data detrended residuals

Figure 7.7 Semivariograms for varying angles θ = 0° to 45°

Figure 7.8 Semivariograms for changing angles θ = 60° to 105°

Figure 7.9 Semivariograms for changing angles θ = 120° to 165°

Figure 7.10 Boxplot of the square root difference cloud

Figure 7.11 Semivariograms for angles θ = 0° and θ = 90°

Figure 7.12 Semivariogram for permeability based on the selected Gaussian‐Gaussian model

Figure 7.13 Kriging prediction for permeability values across reservoir using selected model

Figure 7.14 Semivariogram for permeability based on the exponential model

Figure 7.15 Kriging prediction for permeability values across reservoir using exponential model

Figure 7.16 Knowledge Synthesis foundation

Figure 7.17 Knowledge Synthesis implementing Bayesian maximum entropy methodology

Chapter 08

Figure 8.1 Machine learning taxonomy

Figure 8.2 Concrete and colorful property space concept

Figure 8.3 Red, green, and blue (RGB) color cube

Figure 8.4 Typical open workflow based on derived seismic attributes

Figure 8.5 Supervised and unsupervised data mining techniques in deep learning

Figure 8.6 Deep forward neural network

Figure 8.7 Convolutional deep neural network

Figure 8.8 5 × 5 seismic image convolved with a 3 × 3 matrix to generate another 3 × 3 matrix of pixel values

Figure 8.9 Max‐pooling function applied to demarcate a spatial neighborhood

Figure 8.10 Recurrent deep neural network

Figure 8.11 Stacked denoising autoencoder details

Figure 8.12 Seismic features generated for deep learning methodology

Figure 8.13 Stacked denoising autoencoder

Figure 8.14 Convert pixels of interest to grayscale

Figure 8.15 Break grayscale images into thousands of overlapping patches

Figure 8.16 Illustration of overlapping patches in an image

Figure 8.17 Patch vectorization rearranges pixels as a row vector in lexicographic order

Figure 8.18 Example of a dictionary that has 12 atoms

Figure 8.19 Schematic of a stacked autoencoder. Outputs are set equal to input patterns and the learned weights of the output layer from the dictionary

Chapter 09

Figure 9.1 Reservoir characteristic workflows using DL technologies

Figure 9.2 Classification as applied to the interpretation of seismic facies (Duda et al., 2000)

Figure 9.3 High‐resolution seismic images

Figure 9.4 Sample 20 × 20 pixel patches created from high‐resolution seismic images

Figure 9.5 Four clusters of patches overlaid onto a single original inline 2D seismic image

Figure 9.6 Dictionary defines new direct hydrocarbon indicators (DHIs)

Figure 9.7 Single‐layer autoencoder trained by stochastic gradient descent optimization

Figure 9.8 Sample of the dictionary images

Figure 9.9 Exploratory data analysis visualizing the histograms of key variables

Figure 9.10 RBM architecture implemented to predict output

Figure 9.11 Flat‐spot is a seismic attribute anomaly depicted as a horizontal reflector

Figure 9.12 Simple neural architecture for flat‐spot identification

Figure 9.13 Introduction of layer A focused on a small time–space window

Figure 9.14 Layer of neurons B adds a convolutional layer for focused analysis

Figure 9.15 A max‐pooling layer helps to identify seismic features

Figure 9.16 Workflow details the acquisition of raw hard data from logging tools that are used to determine petrophysical properties

Figure 9.17 Functional data analysis

Figure 9.18 Cluster analysis to segment the well portfolio in a brownfield

Guide

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Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models

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Library of Congress Cataloging‐in‐Publication Data is available:

Names: Holdaway, Keith R., author. | Irving, Duncan H. B., 1971– author.Title: Enhance oil & gas exploration with data‐driven geophysical and petrophysical models / by Keith R. Holdaway, Duncan H.B. Irving.Other titles: Enhance oil and gas exploration with data‐driven geophysical and petrophysical modelsDescription: Hoboken, New Jersey : Wiley, 2018. | Includes bibliographical references and index. |Identifiers: LCCN 2017027921 (print) | LCCN 2017040698 (ebook) | ISBN 9781119302599 (pdf) | ISBN 9781119302582 (epub) | ISBN 9781119215103 (hardback)Subjects: LCSH: Petroleum—Prospecting—Mathematics. | Prospecting—Geophysical methods—Mathematics. | Petroleum—Geology—Mathemaical models. | BISAC: BUSINESS & ECONOMICS / Industries / Energy Industries.Classification: LCC TN271.P4 (ebook) | LCC TN271.P4 H653 2018 (print) | DDC 622/.1828—dc23LC record available at https://lccn.loc.gov/2017027921

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Keith Holdaway: To my patient and loving family, Patricia, my wife, and my children, Elyse and Ian.

Duncan Irving: To Sarah, my wife, and my children, Alfred, Edwin, and Ingrid, who have had to put up with less daddy‐time than normal during this creation. Sorry, and thank you!

Foreword

I vividly remember the first time I met Keith Holdaway. It was 14 years ago, and he was standing in the front row of an analytics conference. He cut a distinctive profile as he challenged the speaker at the podium, asserting quite stubbornly that the oil and gas industry could realize huge returns by using a more data‐driven approach that exploited the full potential of analytics. As a young man (or so I thought of myself at the time), I had been tasked with selling analytical software to upstream oil and gas companies. Coming from a technology background, I realized that this gentleman was the guide I was looking for and made a mental note to seek him out at the cocktail hour.

Back then, in 1989, the digital oilfield was the topic of the day, promising impressive returns. As the industry embraced the concept more fully over the next decade, I observed companies making significant investments in specific data solutions to automate and solve a broad range of problems. Thought leaders eagerly embraced the application of data‐driven analytics, but the adoption was not necessarily as widespread as one would have thought. Scattershot adoption created its issues, with companies sometimes running hundreds of disparate applications and ending up with silos of data across their organizations. The promise remained.

Fast forward to 2014 and Keith's first book, Harness Oil and Gas Big Data with Analytics, which arrived just before crude plunged to historic lows. In retrospect his book seems almost prescient as the industry's enthusiasm for data‐driven analytics has been driven in part by the potential to generate greater value from its assets in the face of a much lower price per barrel. Many of the leading players—and several influential thought leaders among smaller oil companies—have made substantial investments in this area, and there is more to come. Increasingly, I am contacted by clients looking for data scientists, asking for training, and seeking guidance on how best to implement advanced analytics programs. We often point them to Keith's book, among other resources at SAS and elsewhere, to help them validate the best path forward.

Hence the genesis of this new book. Interest in his first book has been consistent enough that colleagues implored Keith to write a second volume: a more particular text that digs deeper into applying data‐driven approaches across the exploration sector. Keith and his colleague, Dr. Duncan Irving, have written an invaluable book, exploring the data‐driven methodologies in the disciplines of geophysics and petrophysics. And the timing is right. We are witnessing an unprecedented convergence of big data and cloud technology with massive increases in computing power at a time when a climate of low prices has made driving efficiencies an absolute requirement. Add to that the influx of technology‐attuned Millennials into the workforce, and oil and gas practitioners are on the verge of a new era of opportunity to transform their business.

I have no doubt that this volume will be a valuable addition to the growing body of resources focused on this exciting area. Over years of working at the nexus of energy and technology, Keith has become a mentor and friend. His colleague is a globally recognized geophysicist working in the field of data analytics and brings innovative ideas to the evolving science of data‐driven and soft‐computing technologies. This new and important book is the result of years of deep work in this area and a real passion for the topic, approached with the same determination I saw at the front of that conference room many years ago. I am honored to introduce this book: Enhance Oil and Gas Exploration with Data‐Driven Geophysical and Petrophysical Models.

Foreword

The oilfield is one of the most data‐rich industries in the world, and concerning real information (as opposed to virtual data generated by the web and other virtual environments) can lay claim to the most data intensive industry. Most organizations, if they are honest with themselves, rarely capitalize on the potential of analytics and ‘big data.’ The authors of this book address the most common pitfalls that beset analytics and provide a comprehensive framework and roadmap, from the exploration and production perspective, to achieve the real goal of analytics—simplifying, expediting, or making possible the translation of data into profitable and sustainable outcomes.

To unleash the power of analytics, one must first understand what they are and are not. Analytics are data‐centric processes that, if designed and executed properly, will lead to insights and outcomes. Each aspect of the process must receive due diligence, and the focus of the endeavor should always be to add value to the organization.

WHAT ANALYTICS IS NOT

The most common mistake when understanding analytics is to confuse the sizzle with the steak—that is to conflate the perception of a thing with the substance of the thing. Many managers and even technical professionals accept the misconception that analytics is the collation and visualization of data using colorful charts and graphs. This is not only incorrect, but there is a tacit danger in this assumption because it can significantly limit future analytic endeavors that do not, per se, yield an attractive visual. It must be understood, therefore, that dashboards and reports are one of many results of analytics and, while they are the most visible, they may not be the most valuable.

WHAT ANALYTICS ARE

Analytics are multi‐step processes which transform data from one or more sources into information which leads to changes in actions and behaviors; and, if an organization is unwilling to do either, investment in analytics should be reconsidered. This book, more than any other before it, details a simple, yet robust, approach to developing an analytics plan that will lead to success. Though analytics methodologies vary depending on query most processes should contain at least the following:

Data Modeling. Analytics planning should ensure, within practical limits, that necessary and sufficient data are identified beforehand.

Data Gathering with a focus on quality. Identification and management of adverse data are often far more resource intensive and problematic than data that is missing. Acquiring real data often involves rigorous technical and contract specifications that include detailed definitions of data properties.

Data Management—how data will be transferred, stored, secured, transformed, and distributed.

Analysis—Understanding which analytical methods are most appropriate based on types of data and questions asked as well as the speed and accuracy of the desired results.

Communication—Determining the most efficient and influential modes in which to communicate data to those who should, or could, consume it—whether it is formal reports, presentations, email, social media, audiovisual, or combination of these and other forms.

Management of Change. Perhaps the most important, yet sadly overlooked, part of an analytics project involves: identifying, before work begins, who all relevant stakeholder (or customers) are, clearly documenting their needs, and agreeing in advance on if, or how, changes to process might occur based on the results of analyses.

Preface

Our motivation for writing this book comes from the professional curiosity and experience we have accumulated over recent years in the Oil and Gas industry. We have noted and continue to witness the struggles between geoscientists and their multiple spatial and temporal datasets. Traditional interpretation can provide certain answers based on Newtonian physics and the fundamental laws of nature, but with so much data being amassed with sensors in this digital age, it is necessary to marry deterministic interpretation with data‐driven workflows and soft‐computing models.

Owing to the cyclical nature of the Oil and Gas industry, we have seen historically depressed crude prices since 2015. This last downturn, like previous historical downturns, shook the industry to the point of an overreaction: people losing their livelihoods, reduction in OPEX, and cancellation of projects, particularly in exploration. It is at these transition points that oil and gas companies seek more efficient work processes and best practices. This invariably results in the adoption of technologies not necessarily new in other industries. Today we see more adoption of soft‐computing and data‐driven analytics to complement the traditional interpretation.

Given these cyclical‐downturn scenarios, we ask ourselves, being in the trough of a current downturn: What's happening in the Oil and Gas industry today?

We are aware of the dramatic drop in crude oil prices that is a driver behind the industry's march toward adopting new technologies such as analytical and soft‐computing workflows. Oil and gas companies realize the climb from the bottom of the cycle is a slow process and has many global and local influences. Too much supply and weak global demand play into a dynamic scenario.

Oil and gas companies are currently contemplating serious near‐term investments to develop global assets, but it behooves the industry to move gingerly. We shall witness an inexorably slow increase in oil prices, with global supply bound by the reduction in reserve development projects over the past few years.

Many talented engineers have left the industry, and the internal organizational vagaries, coupled with inflexible and complex systems, processes, and attitudes could put the breaks on any innovative and evolving methodologies and best practices. IOCs and NOCs are looking seriously at a digitization environment using advanced analytics for the new daily workflows. Service companies, analytics vendors, and in‐house capabilities are emerging to address these needs. This will enable oil and gas companies to weather current and future industry downturns.

We see this book as a contribution to enabling upstream geoscientists in data‐driven analytics in geophysics and petrophysics. We hope it serves to bring together the practitioners of conventional upstream computing workflows with the new breed of data scientist and analyst and generate overlap and common ground so they can understand each other's perspectives, approaches, and role in this new computing landscape.

Acknowledgments

We would like to acknowledge and thank all the contributors to and reviewers of the manuscript, especially Dan Whealing of PGS for running his expert eye across the seismic data portions of the book. Stacey Hamilton of SAS Institute has been an encouraging and patient editor without whom this book would never have been completed. We would like to acknowledge our colleagues in the industry who have given constructive feedback, especially Kathy Ball of Devon Energy and Steve Purves of Euclidity, for ensuring the relevance and applicability of the contents. We wish to recognize the research by Dr. Alexander Kolovos for a section of Chapter 7 (“Knowledge Synthesis”) and by Vipin P. Gupta, Dr. E. Masoudi (Petronas), and Satyajit Dwivedi (SAS Institute) for a section of Chapter 4 (“Production Gap Analysis”).

CHAPTER 1Introduction to Data‐Driven Concepts

“Habit is habit and not to be flung out of the window by any man, but coaxed downstairs a step at a time.”

Mark Twain

INTRODUCTION

Current Approaches

We wish to air some of the more important practical considerations around making data available for data‐driven usage. This could be for static, offline studies or for operationalized, online reviews. We introduce the concept of data engineering—how to engineer data for fit‐for‐purpose use outside the domain applications—and we take the reader from the first baby steps in getting started through to thoughts on highly operationalized data analysis.

A geoscience team will use an extensive collection of methods, tools, and datasets to achieve scientific understanding. The diversity of data spans voluminous pre‐stack seismic to single‐point measurements of a rock lithology in an outcrop. Modeling approaches are constrained by:

Size and scarcity of data

Computational complexity

Time available to achieve a “good enough” solution

Cloud computing

Budget

Workflow lubrication

It is this last constraint that has proven the largest inhibitor to the emergence of a data‐driven approach in exploration and production (E&P). It is a motif for the ease with which data and insight are moved from one piece of software to another.

These constraints have led to a brittle digital infrastructure. This is problematic not only in the individual geoscientific silos but also across the wider domain of E&P. We can potentially exclude a rich array of data types, and restrict innovative methodologies because of the current hardware/software stacks that have evolved symbiotically. The application‐centric landscape undermines E&P solutions that strive to integrate multidimensional and multivariate datasets.

It was not meant to be this way. Back when it all began, it was okay for decisions to be made in an expert's head. High‐performance computers (HPCs) were power tools that gave the expert better images or more robust simulations, but at the end of the workflow, all that number crunching led to a human decision based on the experience of that human and his or her team of peers. Currently, there is too much riding on this approach.

So, how do we become data‐driven if it's hard to get at the data?

Is There a Crisis in Geophysical and Petrophysical Analysis?

There is a movement to adopt data‐driven analytical workflows across the industry, particularly in E&P. However, there is an existing group of Luddites providing not constructive criticism but deliberate and subversive rhetoric to undermine the inevitable implementation of data‐driven analytics in the industry. It is true data scientists sometimes lack experimental data of a robust nature. How certain are we that we can quantify uncertainties? How can we understand the things that manifest themselves in the real world, in the hydrocarbon reservoirs? They argue that without concrete experimental evidence, theory harbors the risk of retreating into metaphysics. Predictive and prescriptive models are only the source of philosophical discourse. It is tantamount to solving the problem of how many leprechauns live at the end of our garden. Science is not philosophy. Thus, without recourse to experiment, geoscientists play in the realm of pure speculation and march to the metaphysical drumbeat of ancient philosophers. The slide into metaphysics is not always clear. The language of the perplexing mathematical algorithms can mask it. Theoretical physics, especially quantum physics, and the theories that underpin the geosciences and E&P engineering disciplines can be jam‐packed with opaque, impermeable, thorny mathematical structures. The Luddites, looking over the soft computing techniques and data‐driven workflows, are betrayed into believing that only the high mathematics and classical physical laws must deliver rigor, a wisdom of the absolute, the lucidity of the variance between right and wrong. No doubt there is rigor. But the answers we get depend so much on the questions we ask and the way we ask them. Additionally, the first principles can be applied incorrectly and the business problem unresolved for the engineers asking the questions.

So, there is no crisis unless we wish to create one. The marriage between traditional deterministic interpretation and data‐driven deep learning and data mining is a union that when established on the grounds of mutual recognition, addresses an overabundance of business issues.

Applying an Analytical Approach

The premise of this book is to demonstrate the value of taking a data‐driven approach. Put simply, if the data could speak for itself, what would you learn beyond what your current applications can tell you?

In the first place, it is the experience of many other industries that statistical context can be established. This could be around testing the validity of an assumed scientific assumption (for example, water flood versus overburden compaction being the cause of a 4D velocity change) or it could be demonstrating whether a set of observations are mainstream or outliers when viewed at the formation, basin, or analog scale.

The current crop of applications:

Lack the computational platform for scale‐out analysis

Can only consume and analyze data for which they have an input filter

Are only able to use algorithms that are available in the code base or via their application programming interfaces (APIs)

We discuss in greater detail ahead how to get G&G (geological and geophysical) data into a useable format, but first let us set the vision of what could be plausible, and this takes us into the world of analytics.

What Are Analytics and Data Science?

Analytics is a term that has suffered from overuse. It means many things in many industries and disciplines but is almost universally accepted to mean mathematical and statistical analysis of data for patterns or relationships.

We use this term in customer‐ and transaction‐rich industries, as well as domains where businesses operate on the thinnest of margins. In the UK in the 1950s, the Lyons Tea Company implemented what we now recognize as centralized business intelligence. It was a digital computer that performed analytics across its empire‐wide supply chain: thousands of teashops and hundreds of bakeries. Their business analytics grew from their ability to understand and articulate their business processes regarding a data model: a description of the relationships between entities such as customer and inventory items. The team that built this system (called Leo) went on to create similar platforms for other organizations and even sell computing space. This presaged the central mainframes of IBM by a decade, the supply chains of Starbucks by four decades, and the cooperation/competition of computing resources pioneered by Amazon. This history is well documented (Ferry, G., 2010, “A Computer called LEO”) and is worth bearing in mind, as we understand how the paradigm applies to the geoscientific domain.

Let us fast‐forward to the late 1990s and the evolution of the Internet beyond its academic and military homelands. Data could be collected from across an organization and transmitted into, around, and beyond its conventional boundaries. This gave businesses no technical reason to avoid emulating Lyons's example of 40 years before, and those that could exploit the ability to process and assimilate their data for business impact pulled ahead of those that proved unwilling or unable to embrace this technical potential. Davenport's “Competing on Analytics” is a mesmerizing overview of this dynamic period in business history (Davenport, Harris, 2007).

As well as the ability to move data around using well‐designed and implemented protocols (i.e., via the Internet), the data was generated by:

Interactions between people and organizations via interfaces such as point‐of‐sale terminals or ATMs

Communications between individuals and agencies via web‐based services

The capture of data along a supply chain as goods and materials—or people in the case of travel and hospitality industries—moved around a complex system

Data arising from a transaction could be captured trivially at sufficient quality and richness to enable statistical insight to be gained, often in real time, in the instance of assessing the likelihood that it is someone other than a banking card's owner using it at a given location and time.

Analytics is provisioned by the integration and contextualization of diverse data types. Moreover, it is predicted by timely access to reliable, granular data. If we look to the downstream domains of our industry, this would be real‐time access to real‐time data about refinery operations and productivity and passing it through to trading desks to enable capacity to be provisioned against spot pricing options.

The economic luxury of $100 oil insulated a lot of the upstream domain from adopting this type of integration. With the growth of factory‐style drilling for unconventional plays, development and lifting costs became a major component of the economics. Since 2014, it has become less unusual (but still not mainstream) for drilling engineers to be guided in their quest for best practices. Such guides include analytical dashboards that are the result of combining petrophysical, technical, and operational data in statistical models. Engineers can use such guidance to characterize likelihoods of bit failure or stuck pipe under given geological and operational parameters.

The big surprise from working on such projects is not the willingness of rough‐necked senior drillers to embrace such an approach (money, especially saved costs, always talks), but more that the data types in question could be brought together and used in such a manner. This combined an approach that used to be called data mining (it's still an appropriate term but is now deeply unfashionable) and soft computing techniques, which currently fall under the definition data science.

To a dyed‐in‐the‐wool data miner (and probably a senior drilling engineer), data science is one of those unpleasant necessities of modern life (so it's probably an age‐related thing). Data science is an umbrella term embracing mathematics, especially statistical expertise, domain understanding, and an intimate knowledge of the domain data and the different format standards. Clearly, this is beyond the capabilities of one single person, hence the widely circulated concept of the data science unicorn.

However, our experiences suggest that such a team should:

Be configured as small as possible

Contain a mathematical component that can cope with the physical sciences

Deal with the worst of formats and the poorest data quality

Data science, done well, has been the difference between liquidity (and at least the next round of venture capital) and history for startups and mega‐scale incumbents in many industries in the twenty‐first century. It may seem, on the first encounter, to be an ad‐hoc, ungoverned approach to working with data and working in general, but it has yielded dividends when applied formally in an organization.

If there is the political will in an organization to accept and act on findings from data science activities, then it will have a quantifiable business impact. Hence, it is reasonable to assume that data science becomes a measurable and valued capability in that organization. It requires a cultural change to provide pervasive impact, but we all must start somewhere, and small bite‐sized projects run with a well‐constrained scope in an agile manner can yield impactful results. The endpoint is a continuous conveyor of insight generation, through business validation and into operational usage, the DevOps mindset.

As an industry, we are a long way from A‐B testing of our processes in the way that online retailers will test different views of their website on statistically sub‐selected groups of their clientele to assess session profitability (yes, they do). There is a lot that can be learned about the behavior of many things that we don't think of in population terms (e.g., wells, formations, offset gathers of traces), and the relationships that may exist within and between such logical or statistical groupings.

Meanwhile, Back in the Oil Industry

With this landscape in sight, let us now turn our gaze to our industry. E&P workflows are designed with the goal of delivering high‐value insights into the subsurface world. Data is acquired often at high cost and contains information of potentially enormous economic value. While the general types of data have not changed much since the first seismic surveys were performed and the first wells drilled, the scale of acquisition has increased by orders of magnitude.

We are still trying to measure the properties and behaviors of the subsurface and the engineering developments, interventions, and operations that we apply to the subsurface. But, in contrast, the time available to provide insight has shortened from years to months, or even weeks and days. Workflows are compressed in response to fit more agile portfolio decision making and operationalized development and production environments.

However, the business units involved in the upstream domain have hardened into brittle silos with their disciplines, processes, and technological predilections with data compartmentalization. There is an old approach to data curation, with lineage and provenance often missing, and this leads to a fundamental lack of trust in data on the rare occasion that there is the political and physical will to move it from one business silo to another.

With each silo being driven by its key performance indicators (KPIs), they can often be working at odds with each other. The information technology (IT) and operational technology (OT) capabilities in each domain have prevented data, used at operational and tactical levels, from being given enterprise visibility and value. Hence, there is no analytical culture in the upstream domain of our industry. (We often turn to the refining and trading domains as occasional beacons of good practice.)

With no data‐driven culture, there is a weak alignment of business challenges across silos and processes, and no analytical capability has emerged at the enterprise scale. The economic upheaval of the 2014/15 price crash stunned the industry and laid bare its inability to respond to challenges at this scale as the underlying processes were so brittle. However, there is a focus emerging on how cost and value can be tied to processes and activities at ever‐more granular scales. This is more predominant in the operations and production domains, but the impact is tangible.

The risk is that the same mistakes are repeated. There is a cultural mistrust between the operational business units and the corporate IT teams that should or could support them. This led to the outsourcing of software development and data processing to proprietary systems from data historians to seismic acquisition and processing. This removal of control over algorithms, data platforms and whole architectures in the case of sensor data yielded control over how data can support a business to the service companies and consultancies and is one of the most notable differences between the Oil and Gas industry and the industries mentioned earlier.

We are in peril of echoing the same mistakes by positioning analytics as point solutions that fail to scale or join up with other analytical endeavors. Without a data‐driven