Accurate real-time prediction of formation pressure and kick detection is crucial for drilling operations, as it can significantly improve decision-making and the cost-effectiveness of the process. Data-driven models have gained popularity for automating drilling operations by predicting formation pressure and detecting kicks. However, the current literature does not make supporting datasets publicly available to advance research in the field of drilling rigs, thus impeding technological progress in this domain. This paper introduces two new datasets to support researchers in developing intelligent algorithms to enhance oil/gas well drilling research. The datasets include data samples for formation pressure prediction and kick detection with 28 drilling variables and more than 2000 data samples. Principal component regression is employed to forecast formation pressure, while principal component analysis is utilized to identify kicks for the dataset's technical validation. Notably, the R2 and Residual Predictive Deviation scores for principal component regression are 0.78 and 0.922, respectively.

Symbolic Machine Learning Prover (SMLP) is a tool and a library for system exploration based on data samples obtained by simulating or executing the system on a number of input vectors. SMLP aims at exploring the system based on this data by taking a grey-box approach: SMLP combines statistical methods of data exploration with building and exploring machine learning models in close feedback loop with the system's response, and exploring these models by combining probabilistic and formal methods. SMLP has been applied in industrial setting at Intel for analyzing and optimizing hardware designs at the analog level. SMLP is a general purpose tool and can be applied to systems that can be sampled and modeled by machine learning models.

Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. This is arguably due to three main factors: powerful computers, new techniques to train deeper networks and larger datasets. Although the first two are readily available in modern computers and ML libraries, the last one remains a challenge for many domains. It is a fact that big data is a reality in almost all fields nowadays, and geosciences are not an exception. However, to achieve the success of general-purpose applications such as ImageNet - for which there are +14 million labeled images for 1000 target classes - we not only need more data, we need more high-quality labeled data. When it comes to the Oil&Gas industry, confidentiality issues hamper even more the sharing of datasets. In this work, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation. The Netherlands F3 dataset acquisition was carried out in the North Sea, Netherlands offshore. The data is publicly available and contains pos-stack data, 8 horizons and well logs of 4 wells. For the purposes of our machine learning tasks, the original dataset was reinterpreted, generating 9 horizons separating different seismic facies intervals. The interpreted horizons were used to generate approximatelly 190,000 labeled images for inlines and crosslines. Finally, we present two deep learning applications in which the proposed dataset was employed and produced compelling results.

Five years from now, company leaders will be looking back and wishing they developed an artificial intelligence strategy sooner, according to one of the veterans of the field. Andrew Ng, the cofounder of Coursera and the former machine learning chief at Chinese tech powerhouse Baidu, said that he thinks Fortune 500 businesses will find the rise of AI similar to the rise of the internet. Some top CEOs bemoan how their businesses were late to the party when it came to competing on the internet, and Ng said that the same thing will be true when it comes to AI. In his view, businesses are best off hiring a leader with deep knowledge of the field who can help build up an organization's knowledge and capabilities in a centralized way. That chief AI officer, as he described it, would be charged with helping to bring expertise in the field to the rest of the a company.

Artificial intelligence may be one of the technology world's current obsessions, but many people find it scary, envisioning robots taking over the world. Two top experts in the field-- Andrew Ng, a Stanford University adjunct professor and former AI scientist at Alphabet Inc.'s Google and Chinese internet giant Baidu Inc., and Tong Zhang, executive director of the AI Lab at Tencent Holdings Ltd. --sat down with The Wall Street Journal's global technology editor, Jason Dean, to explain why they believe the opportunities associated with this technology far outweigh the bad. The title of this panel refers to "the singularity," or the idea that artificial intelligence will become so powerful that robots will take over. Andrew, I know you're skeptical of that. What should we be worried about with AI and where are the biggest opportunities?

Buzzeo (Phoenix, Ariz.), a software engineering firm, has developed a highly adaptable self-service application that automates various administrative Bell Helicopter Textron (Fort Worth, rapid transit (BART) system. The lab functions for the higher-education Tex.), a manufacturer of helicopters, will develop a system to better train marketplace. This rule-based has implemented an intelligent system both new BART operators and those system has helped Buzzeo cut its to automate the procurement needing periodic retraining. ATS enables customers traffic problems at commercial airports. Technical Library at the to track packages through a The $9.3 million, two-story Phillips site on Kirtland Air Force Base, nationwide 800 number by simply building, called the Surface Development New Mexico, is using advanced pattern-recognition stating a tracking number to learn and Test Facility, is being built at technology to design the status of a package.