Journal des sciences de la Terre et du changement climatique

Abstrait

Artificial Intelligence for Lithology Identification through Real-Time Drilling Data

Alireza Moazzeni and Mohammad Ali Haffar

In order to reduce drilling problems such as loss of circulation and kick, and to increase drilling rate, bit optimization and shale swelling prohibition, it is important to predict formation type and lithology in a well before drilling or at least during drilling. Although there are some methods for finding out the lithology such as log interpretation, there is no method for determining lithology before or during drilling by a great degree of accuracy. Determination of formation type and lithology is very complicated and no analytical method is presented for this problem so far. In this situation, it seems that artificial intelligence could be really helpful. Neural networks can establish complicated non-linear mapping between inputs and outputs. In this paper, formation type and lithology of the formation will be predicted using real-time drilling data with an acceptable accuracy, while drilling that formation using artificial neural network. 47500 sets of data from 12 wells in South Pars gas field (in south of Iran) were selected and, after data mining and quality control, were imported to artificial neural networks. Results show that neural networks can determine type of formation and lithology with near 90% accuracy.