Chapter 1
Fundamentals of Soft Computing

There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy.

William Shakespeare: Hamlet

The oil and gas industry has witnessed a compelling argument over the past decade to adopt soft computing techniques as upstream problems become too complex to entrust siloed disciplines with deterministic and interpretation analysis methods. We find ourselves in the thick of a data avalanche across the exploration and production value chain that is transforming data-driven models from a professional curiosity into an industry imperative. At the core of the multidisciplinary analytical methodologies are data-mining techniques that provide descriptive and predictive models to complement conventional engineering analysis steeped in first principles. Advances in data aggregation, integration, quantification of uncertainties, and soft computing methods are enabling supplementary perspectives on the disparate upstream data to create more accurate reservoir models in a timelier manner. Soft computing is amenable, efficient, and robust as well as being less resource intensive than traditional interpretation based on mathematics, physics, and the experience of experts. We shall explore the multifaceted benefits garnered from the application of the rich array of soft computing techniques in the petroleum industry.

CURRENT LANDSCAPE IN UPSTREAM DATA ANALYSIS

What is human-level artificial intelligence? Precise definitions are important, but many experts reasonably respond to this question by stating that such phrases have yet to be exactly defined. Bertrand Russell remarked:

I do not pretend to start with precise questions. I do not think you can start with anything precise. You have to achieve such precision as you can, as you go along.1

The assertion of knowledge garnered from raw data, which includes imparting precise definitions, invariably results from exhaustive research in a particular field such as the upstream oil and gas (O&G) disciplines. We are seeing four major trends impacting the exploration and production (E&P) value chain: Big Data, the cloud, social media, and mobile devices; and these drivers are steering geoscientists at varying rates toward the implementation of soft computing techniques.

The visualization of Big Data across the E&P value chain necessitates the usage of Tukey’s suite of exploratory data analysis charts, maps, and graphs2 to surface hidden patterns and relationships in a multivariate and complex upstream set of systems. We shall detail these visual techniques in Chapters 3, 4, and 9 as they are critical in the data-driven methodologies implemented in O&G.

Artificial neural networks (ANN), fuzzy logic (FL), and genetic algorithms (GA) are human-level artificial intelligence techniques currently being practiced in O&G reservoir management and simulation, production and drilling optimization, real-time drilling automation, and facility maintenance. Data-mining methodologies that underpin data-driven models are ubiquitous in many industries, and over the past few years the entrenched and anachronistic attitudes of upstream engineers in O&G are being diluted by the extant business pressures to explore and produce more hydrocarbons to address the increasing global demand for energy.

Digital oilfields of the future (DOFFs) and intelligent wells with multiple sensors and gauges are generating at high velocity a plethora of disparate data defining a complex, heterogeneous landscape such as a reservoir-well-facility integrated system. These high-dimensionality data are supplemented by unstructured data originating from social media activity, and with mobile devices proving to be valuable in field operations and cloud computing delivering heightened flexibility and increased performance in networking and data management, we are ideally positioned to marry soft computing methodologies to the traditional deterministic and interpretive approaches.

Big Data: Definition

The intention throughout the following pages is to address the challenges inherent in the analysis of Big Data across the E&P value chain. By definition, Big Data is an expression coined to represent an aggregation of datasets that are voluminous, complex, disparate, and/or collated at very high frequencies, resulting in substantive analytical difficulties that cannot be addressed by traditional data processing applications and tools. There are obvious limitations working with Big Data in a relational database management system (DBMS), implementing desktop statistics and visualization software. The term Big Data is relative, depending on an organization’s extant architecture and software capabilities; invariably the definition is a moving target as terabytes evolve into petabytes and inexorably into exabytes. Business intelligence (BI) adopts descriptive statistics to tackle data to uncover trends and initiate fundamental measurements; whereas Big Data tend to find recreation in the playgrounds of inductive statistics and concepts from nonlinear system identification. This enables E&P professionals to manage Big Data, identify correlations, surface hidden relationships and dependencies, and apply advanced analytical data-driven workflows to predict behaviors in a complex, heterogeneous, and multivariate system such as a reservoir. Chapter 2 discusses Big Data in more detail and the case studies throughout the book will strive to define methodologies to harness Big Data by way of a suite of analytical workflows. The intent is to highlight the benefits of marrying data-driven models and first principles in E&P.

First Principles

What are first principles? The answer depends on your perspective as an inquisitive bystander. In the field of mathematics, first principles reference axioms or postulates, whereas in philosophy, a first principle is a self-evident proposition or assumption that cannot be derived from any other proposition or assumption. A first principle is thus one that cannot be deduced from any other. The classic example is that of Euclid’s geometry that demonstrates that the many propositions therein can be deduced from a set of definitions, postulates, and common notions: All three types constitute first principles. These foundations are often coined as a priori truths. More appropriate to the core message in this book, first principles underpin the theoretical work that stems directly from established science without making assumptions. Geoscientists have invariably implemented analytical and numerical techniques to derive a solution to a problem, both of which have been compromised through approximation.

We have eased through history starting thousands of years ago when empirical models embraced our thinking to only a few centuries ago when the landscape was populated by theoretical intelligentsia espousing models based on generalizations. Such luminaries as Sir Isaac Newton, Johannes Kepler, and James Clerk Maxwell made enormous contributions to our understanding of Mother Nature’s secrets and by extension enabled the geoscientific community to grasp fundamentals that underpin physics and mathematics. These fundamentals reflect the heterogeneous complexity inherent in hydrocarbon reservoirs. Only a few decades have passed since we strolled through the computational branch of science that witnessed the simulation of complex systems, edging toward the current landscape sculpted by a data-intensive exploratory analysis, building models that are data driven. Let the data relate the story. Production data, for example, echo the movement of fluids as they eke their way inexorably through reservoir rocks via interconnected pores to be pushed under natural or subsequently fabricated pressures to the producing wells. There is no argument that these production data are encyclopedia housing knowledge of the reservoirs’ characterization, even if their usefulness is directly related to localized areas adjacent to wells. Thus, let us surface the subtle hidden trends and relationships that correlate a well’s performance with a suite of rock properties and influential operational parameters in a complex multivariate system. Geomechanical fingerprints washed in first principles have touched the porous rocks of our reservoirs, ushering the hydrocarbons toward their manmade conduits. Let us not divorce first principles, but rather marry the interpretative and deterministic approach underscored by our scientific teachings with a nondeterministic or stochastic methodology enhanced by raw data flourishing into knowledge via data-driven models.

Data-Driven Models

The new model is for the data to be captured by instruments or to be generated by simulations before being processed by software and for the resulting information and knowledge to be stored in computers.3

Jim Gray

Turning a plethora of raw upstream data from disparate engineering disciplines into useful information is a ubiquitous challenge for O&G companies as the relationships and answers that identify key opportunities often lie buried in mountains of data collated at various scales in depth as well as in a temporal fashion, both stationary and non-stationary by nature.

O&G reservoir models can be characterized as physical, mathematical, and empirical. Recent developments in computational intelligence, in the area of machine learning in particular, have greatly expanded the capabilities of empirical modeling. The...