Coal identification using neural networks with real-time coalbed methane drilling data
Ruizhi Zhong A C , Raymond JohnsonA School of Chemical Engineering, University of Queensland, Brisbane, Qld 4072, Australia.
B School of Mechanical and Mining Engineering, University of Queensland, Brisbane, Qld 4072, Australia.
C Corresponding author. Email: r.zhong@uq.edu.au
The APPEA Journal 59(1) 319-327 https://doi.org/10.1071/AJ18091
Submitted: 7 December 2018 Accepted: 28 January 2019 Published: 17 June 2019
Abstract
Currently, coal is identified using coring data or log interpretation. Coring is the most dependable methodology, but it is costly and its characterisation is expensive and time consuming. Logging methods are convenient, reliable, and reproducible, but can be subject to statistical and shouldering effects and often have operational difficulties in deviated or horizontal wells. Drilling data, which are routinely available, can potentially be used to identify coal sections in a machine learning environment when conventional wireline logs are not available.
To achieve this, a four-layer artificial neural network (ANN) was used to identify coals in a well at Walloon Sub-Group, Surat Basin. The ANN model used drilling data and some logging-while-drilling (LWD) data. The inputs for the lithological model from high-frequency drilling data include weight on bit, rotary speed, torque, and rate of penetration. Inputs from LWD data include gamma ray and hole diameter. The criterion for coal identification is based on bulk density cutoff.
The simulation results show that the ANN can deliver an overall accuracy of 96%. Due to the low net-to-gross ratio of coals within the Walloon sequence, a lower but reasonable F1 score of 0.78 is achievable for the coal sections. The proposed model can potentially be implemented in real-time to identify coal intervals without additional logs and aid validation of minimal log data.
Keywords: artificial neural network (ANN), coal identification, coal seam gas, drilling data, machine learning.
Ruizhi Zhong is a postdoctoral research fellow in the School of Chemical Engineering at the University of Queensland. His research interests include drilling engineering, machine learning, hydraulic fracturing, and geomechanics. His recent work involves the application of machine learning for drilling applications. He holds a PhD in Petroleum Engineering from the University of Tulsa and an MS in Petroleum and Natural Gas Engineering from West Virginia University. He is a member of Society of Petroleum Engineers (SPE). |
Raymond (Ray) L. Johnson, Jr. is presently Professor of Well Engineering and Production Technology at the University of Queensland, School of Chemical Engineering, and serves as Adjunct Associate Professor at the University of Adelaide. He has a PhD in Mining Engineering, an MSc in Petroleum Engineering, a Graduate Diploma in Information Technology, and a BA in Chemistry. Ray has been active in the SPE, past chair of the SPE Queensland Section, 2013 and 2015 co-Chair of the SPE Unconventional Reservoir Conference and Exhibition Asia Pacific, and Technical Award Recipient of SPE Regional Awards in 2011 and 2017. He has been actively involved as an author and researcher in the areas of reservoir geomechanics, hydraulic fracture design execution and evaluation, and unconventional resource development. |
Dr Zhongwei Chen gained his PhD in Petroleum Engineering from the University of Western Australia in September 2012. He joined the University of Queensland as an associate lecturer in January 2013, and is currently a senior lecturer in mining engineering at UQ School of Mechanical and Mining Engineering. His research interests are in the areas of unconventional geomechanics, fluid flow in fractured porous media, and the coupled computational modelling associated with unconventional gas extraction, CO2 sequestrations, and underground coal mining operations. He is the recipient of American Rock Mechanics Research Award for 2011, a member of SPE and AusIMM, and has served as an associate editor of International Journal of Oil, Gas and Coal Technology since 2015. |
Nathaniel Chand is a data analyst in the School of Chemical Engineering at the University of Queensland. His main interests include machine learning, operations research, and data analytics. His recent work involves identifying pressure-dependent effects on coal permeability and productivity indices. He holds a BE in Mechanical Engineering and a BSc in Mathematics from the University of Queensland. |
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