Data & Analytics

Natural-Language-Processing Techniques for Oil and Gas Drilling Data

This paper presents a method to compare the distribution of hypothesized and realized risks to oil wells described in two data sets that contain free-text descriptions of risks.

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Recent advances in search, machine learning, and natural-language processing have made it possible to extract structured information from free text, providing a new and largely untapped source of insight for well and reservoir planning. However, major challenges are involved in applying these techniques to data that are messy or that lack a labeled training set. This paper presents a method to compare the distribution of hypothesized and realized risks to oil wells described in two data sets that contain free-text descriptions of risks.

Introduction

In the oil and gas industry, risk identification and risk assessment are critical. This holds particularly true during the drilling stages, which cannot begin before a risk assessment is conducted.

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