Welcome to EPTS - Exploration and Production Training Services

EPTS specialises in the provision of technical courses covering a broad range of subsurface topics related to oil/gas field exploration and development. The company was established in 1999 and comprises a group of highly experienced E&P professionals with a proven track record of technical excellence based on many years EP industry experience and a strong academic foundation. They have enthusiasm for their profession, enjoy knowledge transfer and have excellent teaching capabilities. Most of our instructors have lived in different countries across the globe and have worked many years in a variety of E&P environments. Since the company's foundation, EPTS' trainers have taught hundreds of well received courses to a large number of oil industry clients across all continents.

Check out our Course Portfolio, our Teaching Experts, or contact us for more information or to discuss your specific needs

All our courses can be modified to be taught remotely, led by an instructor in a virtual teaching environment. Check out the description of "Virtual" Courses already available

EPTS does not possess its own training facilities, but all courses can be booked by companies to be given in-house and most courses feature on the open course calendars of our partners, see links below.

Highlights

Venture to do this Machine Learning Quiz
ChatGPT in courses

Another interesting experiment with ChatGPT. There are several ways of using Large language Models. the answers to a simple question: “What geophysical methods are available”, have been compared using ChatGTP3, ChatGPT4 and Bing ChatGPT. Instead of ChatGPT4 a link was made with ChatGPT3.5. ChatGTP3: The answer was quite comprehensive. Only surface and Scholte waves were missing under seismic. MT should have been mentioned explicitly under Electrical or Magnetic methods. Hyperspectral imaging under remote sensing might have included radiometric surveys. ChatGPT3.5: The answer is far from complete: gravity, remote sensing and GPR are not mentioned. Interesting is that the sources of information are explicitly mentioned (with links). Bing ChatGPT: The answer is based on Wikipedia and the US Environmental Protection Agency. It provided the least information. Note that Bing AI has additional features that ChatGPT does not have, such as the use of images, which haven’t been tried. Conclusion: For the time being ChatGPT3 will be used in courses. See for answers https://breakawaylearning.nl/
De-risking CO2 storage

Seismic imaging stands out with its ability to attain high resolution, area-wide coverage, and high fidelity. An often-mentioned requirement for time-lapse monitoring of CO2 storage (or hydrocarbon production) is that the seismic acquisition should be repeated as accurately as possible, which is costly and time-intensive. However, there is an alternative. A synthetic data set based on a North Sea (Blunt) sandstone reservoir and a seal (Rot Halite) was created. Using a rock physics model, the reservoir porosity and permeability were calculated based on the velocity and density. As a result of the injection pressure, fractures in the seal were created, leading to two model sets: with leakage and without leakage. Shot gathers and time-lapse sections were generated for the two different sets of models. The usual method for evaluating the effectiveness of time-lapse seismic is by calculating the Normalised-Mean-Root-Square metric (NMRS). For the synthetic data the NRMS errors for the Joint and Separate inversions were 2.30% and 8.48%, respectively, which shows that joint inversion is to be preferred. Also the spread for the joint inversion is much lower. Then a highly sensitive Deep Neural Classifier (Vision Transformer, ViT) was trained to classify seismic into leakage and no leakage (regular). The trained network was applied to classification of 206 unseen test instances, with the following results: Predictions TP=True Positive FP=False Positive TN=True negative FN=False Negative Precision=TP/(TP+FP )= 91.8% Recall=TP/(TP+FN) ==43.2% F1=TP/(TP+½(FN+FP)) =58.8% Conclusion: Using Deep Neural Networks yields a significant improvement in NMRS values, without requiring replication of the baseline survey. While the classification results are excellent, there are still False Negatives (FN). This might be acceptable as the decision to stop injection of CO2 should also be based on other information, such as pressure drop at the well head. For more detail see “Geophysical curiosities” on my website www.breakawaylearning.nl. Ref: Ziyi Yin, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, Felix J. Herrmann, arXiv:2211.03527v1