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Front Cover -- DATA ROOM MANAGEMENT FOR MERGERS AND ACQUISITIONS IN THE OIL AND GAS INDUSTRY -- DATA ROOM MANAGEMENT FOR MERGERS AND ACQUISITIONS IN THE OIL AND GAS INDUSTRY -- Copyright -- Dedication -- Contents -- About the Author -- Preface -- Acknowledgments -- 1 - Due diligence, definitions, and doubt -- 1.1 What is due diligence? -- 1.2 Assessing volume and uncertainty alongside value and risk -- 1.2.1 Ranges of recoverable petroleum volumes -- 1.2.2 Potential value of produced petroleum -- 1.2.3 Risks associated with petroleum resource exploration and development -- 1.3 Systems for reporting petroleum resources -- 1.3.1 Why is a resource definition needed? -- 1.3.2 Definition of reserves -- 1.3.2.1 What volumes are not reserves? -- 1.3.3 Sources of risk and uncertainty -- 1.3.4 Deterministic and probabilistic terminologies for reserves estimation -- 1.4 How reliable is reporting of resources? -- 1.5 Requirement for doubt and skepticism -- 1.5.1 Overcoming bias to ensure data quality -- 1.5.2 What is reasonable certainty? -- 1.5.3 As many answers as there are auditors -- 2 - The data room -- 2.1 What is a data room? -- 2.1.1 Physical data room -- 2.1.2 Virtual data room -- 2.1.3 VDR and PDR combination -- 2.1.4 Virtual Physical data room -- 2.1.5 Designated VDR downloader -- 2.2 Why are data rooms important? -- 3 - Data room documentation -- 3.1 Need for a common language between sellers and buyers -- 3.2 The teaser -- 3.3 The confidentiality agreement -- 3.3.1 Definition of what is and what is not confidential information -- 3.3.2 Caveat emptor disclaimer -- 3.3.3 Mental impressions exclusion -- 3.3.4 Return or destruction of data -- 3.3.5 Time limit for CA -- 3.3.6 Breach of CA and choice of law -- 3.4 The information memorandum -- 3.4.1 The essence of an IM -- 3.4.2 Reading between the lines of an IM -- 3.4.2.1 Offering procedure.

Front Cover -- Applications of Artificial Intelligence Techniques in the Petroleum Industry -- Copyright Page -- Contents -- About the author -- 1 Introduction -- 1.1 Overview -- 1.2 Preprocessing of data -- 1.2.1 Data cleaning -- 1.2.2 Data integration -- 1.2.3 Data transformation -- 1.2.4 Data reduction -- 1.2.5 Data discretization -- 1.2.6 Data statistics -- 1.2.6.1 Skewness -- 1.2.6.2 Kurtosis -- 1.3 Processing of data -- 1.3.1 Data training -- 1.3.2 Data validation and testing -- 1.4 Postprocessing of data -- 1.4.1 Statistical analyses for models' evaluation

[v. 1]. Manufacturing -- [v. 2.]. Insurance -- [v. 3]. Transportation -- [v. 4]. Utilities -- [v. 5]. Banking -- [v. 6]. Distribution -- [v. 7]. Education -- [v. 8]. Government. ; Mode of access: Internet.

□ In summary, the introduction of blind persons into the field of data processing gives us much to take satisfaction in and much to be proud of. We must, however, work toward the day when the present attitudes and concerns of all levels of management are no longer a problem. From there we must move on to be sure that blind programmers have the same opportunities in supervision and management as others. The success we've had is proof that employment of the blind in data processing is a good, sound business proposition. From a selfish point of view, there is tremendous public relations value to the company that employs blind persons in data processing. The story of our success has spread far and wide. We have probably received as much favorable publicity on this as any other thing we have done. We regularly get calls from all over the country where others are considering the employment of a blind person. There has also been a positive effect on our own employees. It is one more thing that has given them pride in their company.