Abstract
The updating of reservoir geological models has become a research hotspot. Nevertheless, two difficulties continue to hinder the development of reservoir geological model updating techniques. First, logging while drilling (LWD) is used mainly to guide geosteering operations and effectively identify the lithology; few scholars have researched the interpretation of reservoir physical characteristics while drilling, which is the basis of updating geological models. Second, interpretation results are difficult to transmit to geological models in real time. Based on the LWD technique, this paper uses logging interpretation, machine learning, computer science, and reservoir geological modeling theories and methods to conduct the research of real-time geological model updating around the well. First, based on effective logging data, two machine learning algorithms which are random forest (RF) and extreme gradient boosting tree (XGBoost) are used to establish interpretation models of the reservoir lithology, porosity, and permeability. The parameters of each model are optimized through cross-validation method, and LWD data are interpreted in real time by interpretation models. Second, based on the convenience of the Ocean secondary development platform and the functionality of Petrel software, a real-time transmission plug-in for the current well trajectory and reservoir interpretation results is compiled, and an automatic updating module for the geological model is established. A case study is performed with data from the Sulige gas field in the Ordos Basin, China. For the real-time interpretation of reservoir characteristics while drilling, after 228 trials, the XGBoost algorithm is chosen to establish reservoir lithology, porosity, and permeability interpretation models. For the real-time updating of the geological model around the well, given the consistent probability distributions and the agreement between adjacent wells, we obtain relative errors between the simulated and real values of the lithofacies, porosity, and permeability of 3.90%, 4.50%, and 7.60%, respectively. Therefore, this paper provides a new method for the real-time modification and updating of reservoir geological models, which preliminarily resolved the contradiction between accuracy and real time of geological model real-time updating.
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Funding
This work is supported by (1) National Natural Science Foundation of China (NSFC) (No. 51974248 and No. 51704235), (2) Open Fund (PLC20190702) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology), (3) Open Fund of Shanxi Key Laboratory of Carbon Dioxide Storage and Enhanced Oil Recovery (YJSYZX20SKF0008), and (4) Natural Science Basic Research Plan in Shanxi Province of China (2019JQ-407). (5) Natural Science Basic Research Plan in Shanxi Province of China (2021JQ-601).
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No conflict of interest exits in the submission ofthis manuscript, and manuscript is approved by all authors for publication. Iwould like to declare on behalf of my co-authors that the work described wasoriginal research that has not been published previously, and not underconsideration for publication elsewhere, in whole or in part. All the authorslisted have approved the manuscript that is enclosed. I hope this paper issuitable for “Arabian Journal of Geosciences”.
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Sun, J., Zhang, R., Chen, M. et al. Real-time updating method of local geological model based on logging while drilling process. Arab J Geosci 14, 746 (2021). https://doi.org/10.1007/s12517-021-07034-1
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DOI: https://doi.org/10.1007/s12517-021-07034-1