Improvement of petrophysical workflow for shear wave velocity prediction based on machine learning methods for complex carbonate reservoirs

• Published in: Journal of petroleum science & engineering 2020-09, Vol.192, p.107234, Article 107234
• Main Authors: Zhang, Yan, Zhong, Hong-Ru, Wu, Zhong-Yuan, Zhou, Heng, Ma, Qiao-Yu
• Format: Article
• Language: English
• Subjects: Carbonate reservoirs; LSTM; Petrophysical model; Shear wave velocity prediction; Wireline logs
• ISSN: 0920-4105; 1873-4715
• DOI: 10.1016/j.petrol.2020.107234

Shear wave velocity (S-wave velocity or Vs) is one of the most critical issues for carbonate reservoirs characterization, because of its complexity of rock compositions and pore structures. The Xu-Payne petrophysical model is a commonly used method to predict S-wave velocity. However, the model excessively depends on the interpretation accuracy of rock compositions, pore structures, fluid properties and fluid saturation, which makes the prediction result potentially uncertain. With the development of intelligent algorithms, a machine learning method (Long-short Term Memory Neural Network, LSTM) is proposed to improve the traditional petrophysical workflow. With the capabilities of deep learning and data mining, the LSTM model can deeply mine the rich information in the wireline logs and then establish the relationships between S-wave velocity and wireline logs. In order to illustrate the prediction effect, the carbonate reservoir in the Majiagou Formation Member 5(Ma 5 Member) which belongs to the Ordovician system in the D area of Sulige gas field is taken as an example. Six kinds of sensitive wireline logs, including compensated acoustic log (AC), natural gamma ray log(GR), photoelectric absorption cross-section log (PE), density log (DEN), deep lateral resistivity log(RLLD), neutron log(CNL), are selected as the input data of the LSTM model. The results reveal that the accuracy of LSTM method is up to 98.9%, whereas the Xu-Payne model is only 73%. With the predicted result of all wells in the areas using LSTM, the sensitive elastic properties, including P-and S-wave velocity, P- and S-wave impendences, ratio of P- and S-wave velocity, Poisson's ratio, lame coefficient and fluid sensitivity factor, are also concluded. Compared with the Xu-Payne model, the newly proposed workflow is proved to be convenient and suitable for the prediction of S-wave velocity in carbonate reservoirs. •A data mining method is used to predict S-wave velocity based on the wireline logs and petrophysical model.•Comparison showed superiority of LSTM algorithm to petrophysical model.•The influencing factors for petrophysical model and the LSTM model are deeply analyzed.•Implementation of LSTM model for wells is calculated and the sensitivity of elastic properties are analyzed.•The LSTM model can be widely used in the carbonate reservoirs with different lithology.