Random forest

Drilling

Engineering

Lithology

Biology

Identification (biology)

Support vector machine

Petroleum engineering

Telecommunications

Classifier (UML)

Data mining

Computer science

Botany

Geology

Mechanical engineering

Wireline

Petrology

Drilling fluid

Reservoir modeling

Pattern recognition (psychology)

Artificial intelligence

Wireless

Logging while drilling

Machine learning

Journal of Petroleum Science and Engineering

The identification of lithology from well log data is an important task in petroleum exploration and development. However, due to the complexity of the sedimentary environment and reservoir heterogeneity, the traditional lithology identification methods can not meet the needs of real-time and accurate prediction and identification with logging while drilling (LWD) equipment. The basic data of this paper are derived from conventional wireline logging (CWL) data and the LWD data in Yan'an Gas Field. The main research goal is to compare and analyse three popular machine learning algorithms, which are one-versus-rest support vector machines (OVR SVMs), one-versus-one support vector machines (OVO SVMs) and random forest (RF), and to optimize a more practical method in the field for LWD systems. To reduce the dimensions of the input data, the characteristic parameters of the training data are obtained by a correlation analysis of the logging data. The optimal parameter values of each algorithm are determined by grid search method and 10-fold cross-validation method. On this basis, the lithology predictions of the actual LWD data are carried out by using three classifiers. Considering the time consumption of the model training and the lithology identification accuracy of the model, the best lithology identification model while drilling is selected. The results show that the characteristic parameters of the training data after the correlation analysis are AC, CAL, GR, K, RD and SP logs. The overall classification and recognition performance of the RF classifier is better than that of the other two classifiers, and its accuracy is even greater than 90%. The evaluation matrix shows that the OVR SVMs and RF classifiers yield lower prediction errors than the OVO SVMs classifier in each single lithology identification, but the RF classifier spends much less time in the training process. Based on the comprehensive comparative analysis, it is considered that the RF classifier has the characteristics of a short training time and high recognition accuracy in practical production applications, so it is an ideal optimization classifier for lithology identification while drilling. The research results provide not only a theoretical basis for the drilling geosteering of oilfield development but also valuable information for future basic research.

Optimization of models for a rapid identification of lithology while drilling - A win-win strategy based on machine learning