The continental United States is jam-packed with reminders of our ravenous oil appetite. Since the 1850s, there have been an estimated 3.5 million oil and gas wells drilled across the country. Many of those were abandoned after the companies running them ran out of business or otherwise ceased operating. These forgotten fossil fuel artifacts, referred to officially as "undocumented orphan wells" (UOWs) are often left behind without meaningful efforts taken to safely seal them. Unplugged orphan wells can leak out dangerous methane, oil, and other chemicals for years which can pollute the air and potentially contaminate nearby water sources.

It is very difficult to forecast the production rate of oil wells as the output of a single well is sensitive to various uncertain factors, which implicitly or explicitly show the influence of the static, temporal and spatial properties on the oil well production. In this study, a novel machine learning model is constructed to fuse the static geological information, dynamic well production history, and spatial information of the adjacent water injection wells. There are 3 basic modules in this stacking model, which are regarded as the encoders to extract the features from different types of data. One is Multi-Layer Perceptron, which is to analyze the static geological properties of the reservoir that might influence the well production rate. The other two are both LSTMs, which have the input in the form of two matrices rather than vectors, standing for the temporal and the spatial information of the target well. The difference of the two modules is that in the spatial information processing module we take into consideration the time delay of water flooding response, from the injection well to the target well. In addition, we use Symbolic Transfer Entropy to prove the superiorities of the stacking model from the perspective of Causality Discovery. It is proved theoretically and practically that the presented model can make full use of the model structure to integrate the characteristics of the data and the experts' knowledge into the process of machine learning, greatly improving the accuracy and generalization ability of prediction.

This is a theoretical paper, as a companion paper of the plenary talk for the same conference ISAIC 2022. In contrast to the author's plenary talk in the same conference, conscious learning (Weng, 2022b; Weng, 2022c) which develops a single network for a life (many tasks), "Deep Learning" trains multiple networks for each task. Although "Deep Learning" may use different learning modes, including supervised, reinforcement and adversarial modes, almost all "Deep Learning" projects apparently suffer from the same misconduct, called "data deletion" and "test on training data". This paper establishes a theorem that a simple method called Pure-Guess Nearest Neighbor (PGNN) reaches any required errors on validation data set and test data set, including zero-error requirements, through the same misconduct, as long as the test data set is in the possession of the authors and both the amount of storage space and the time of training are finite but unbounded. The misconduct violates well-known protocols called transparency and cross-validation. The nature of the misconduct is fatal, because in the absence of any disjoint test, "Deep Learning" is clearly not generalizable.

The authors apply a novel deep-learning algorithm called a transformer to build surrogate models for simulations of well performance. Transformer architecture initially was developed for natural-language processing problems. However, in recent years, researchers have adapted transformers for time-series forecasting.

Over the past decade terms such as "Data Science", "Big Data", "Data Lake", "Machine Learning", "AI" and so forth have risen to the forefront (and sometimes fallen back again) of the everyday vocabulary used in the widest variety of industries. I do not wish to engage in an extended argument on consistent nomenclature, but there are two frequently used terms that are of particular interest to me: "Data Scientist" and "Machine Learning Engineer". In the broadest possible sense, both of these terms could be understood as referring to "technically skilled people who build machine learning solutions". "Data Scientist" is a term that over the years has become associated with a sort of generalist mathematician or statistician who can also code a bit and knows how to interpret and visualise data. More recently, the term "Machine Learning Engineer" has become associated with software developers who have picked up some mathematics along the way.

A Regina-based tech startup says it's the first company to use artificial intelligence (AI) to detect leaks at oil wells and pump jacks. In the past, oil and gas companies have used staff to drive to oil wells to inspect them for any issues, such as leaks. One solution is using remote cameras to monitor oil wells, but it results in hundreds or thousands of photos being taken that have to be inspected by employees. Founded in 2018, Wave9 takes the arduous task of inspecting those photos and hands it off to AI. Cameras and sensors placed on pump jacks are processed by the software. The user can then be alerted to issues through apps that run on tablets and smartphones.

The potential of Artificial Intelligence is already being discovered by many industries, including the Oil and Gas, which is investing majorly in Artificial Intelligence and other data technologies with a goal to secure their future competitiveness in a fast-changing environment. Oil is one of the most precious commodities in the energy sector. With the rise in the oil prices and depleting crude oil levels globally, organizations involved in the oil and gas industry are now turning towards modern technologies, specifically Artificial Intelligence, to maximize and optimize their efficiency and revenues. Companies involved in the oil and gas industry leverage AI to drill and mine raw hydrocarbons and other products required to produce fuel. AI helps these companies by developing algorithms that provide accurate and precise intelligence to guide drills on water and land.

With the help of artificial intelligence, BP says it needs 40% fewer workers to keep its natural gas ... [ ] flowing in Wyoming. A visitor to one of BP's natural gas fields in Wyoming a few years ago might have noticed an odd sight: smartphones in plastic bags tied to pumps with zip ties. This was an early test of a multistate initiative by the oil giant to link a network of Wi-Fi sensors to an artificial intelligence system--one that now operates the Wamsutter field in Wyoming with far less human oversight than before. Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry's 150,000 U.S. workers. Giant energy companies like Shell and BP are investing billions to bring artificial intelligence to new refineries, oilfields and deepwater drilling platforms.

In a recent manufacturing industry insights survey on artificial intelligence (AI), 44 percent of respondents from the automotive and manufacturing sectors classified AI as "highly important" to the manufacturing function in the next five years, while almost half--49 percent--said it was "absolutely critical to success." Yet, in many cases, AI is hard to comprehend for manufacturers, as the technology industry has painted it with such a wide brush that few actually understand how it becomes instantiated--beyond some omnipotent source delivering better business results. Manufacturers may actually view AI as highly complex and expensive, requiring end-to-end systems throughout their whole company to work properly, and this translates to a costly overhaul of their entire IT/OT operation. The reality is, AI is much more focused and achievable. AI can work on factory floors with minimal construction and get connected to machines via the Industrial Internet of Things (IIoT).

With the help of artificial intelligence, BP says it needs 40% fewer workers to keep its natural gas flowing in Wyoming. A visitor to one of BP's natural gas fields in Wyoming a few years ago might have noticed an odd sight: smartphones in plastic bags tied to pumps with zip ties. This was an early test of a multistate initiative by the oil giant to link a network of Wi-Fi sensors to an artificial intelligence system--one that now operates the Wamsutter field in Wyoming with far less human oversight than before. Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry's 150,000 U.S. workers. Giant energy companies like Shell and BP are investing billions to bring artificial intelligence to new refineries, oilfields and deepwater drilling platforms.