High-Frequency Drilling Data Analysis to Characterize Water-Ice on the Moon
- Deep Joshi (Colorado School of Mines) | Alfred Eustes (Colorado School of Mines) | Jamal Rostami (Colorado School of Mines) | Jenna Hanson (Colorado School of Mines) | Christopher Dreyer (Colorado School of Mines)
- Document ID
- Society of Petroleum Engineers
- IADC/SPE International Drilling Conference and Exhibition, 3-5 March, Galveston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. IADC/SPE International Drilling Conference and Exhibition
- 1.6 Drilling Operations, 1.11 Drilling Fluids and Materials, 1.6 Drilling Operations, 1.10 Drilling Equipment, 7.6.4 Data Mining, 7.6 Information Management and Systems, 1.11.7 Cuttings Transport, 1.12 Drilling Measurement, Data Acquisition and Automation, 1.12.6 Drilling Data Management and Standards, 7 Management and Information, 1.10 Drilling Equipment
- Drilling data analytics, Pattern Recognition, Drilling automation, In-situ Space Resources Utilization, Lunar Drilling
- 2 in the last 30 days
- 77 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
This paper discusses the evolution of a pattern recognition algorithm that utilizes the high-frequency drilling data to characterize the water-ice on the Moon. The algorithm developed here can estimate the moisture content of a grout sample by analyzing the trend of drilling data. Such an algorithm can be used by NASA and private organizations in near-future to identify and produce water from Lunar Poles.
An auger based rotary drilling rig with a drilling data acquisition system was designed and fabricated. The data acquisition system records drilling parameters like RPM, drilling depth, torque, and weight on bit at 1000Hz. The data is then filtered to remove electromagnetic interference. The drilling tests were conducted on meticulously designed grout block samples which contained the lunar soil simulant with specific geotechnical properties, replicating lunar subsurface at a specific pressure, temperature condition, and moisture contents. Simultaneously, the UCS of the grout block was tested in a lab to correlate the drilling data to specific grout strength.
The drilling data recorded in the homogenous samples and the data collected during UCS tests were used to develop and validate a pattern recognition algorithm. The algorithm was tested on layered grout samples containing layers with varying strength. The UCS estimated by this algorithm was then correlated to the moisture content using the lab observations and published literature. The algorithm could distinctly detect the boundaries between different layers, estimate the UCS, and moisture content in real-time. The high-frequency drilling data was also used to identify different dysfunctions and optimize drilling operations. Trends of MSE, RPM, and torque were analyzed to detect auger choking, inefficient cuttings transport, and drilling vibrations.
On the Moon, this algorithm can be invaluable in optimizing the drilling operations, estimating the spatial distribution of various subsurface layers improving our understanding of the subsurface lunar stratigraphy. It can also be used to estimate the quantity of water available on the lunar poles, which will be essential in planning the manned and robotic lunar missions in the coming years.
|File Size||1004 KB||Number of Pages||12|
Foust, Jeff. "Luxembourg Adopts Space Resources Law." https://spacenews.com/luxembourg-adopts-space-resources-law/.
Joshi, Deep, Alfred Eustes, Jamal Rostami, Colby Gottschalk, Christopher Dreyer, Wenpeng Liu, Zachary Zody, and Claire Bottini. "How Can Drilling Engineers Help Revolutionize Space Transport and Colonize the Solar System: Focusing on Lunar Water-Ice." In SPE Annual Technical Conference and Exhibition. Calgary, Alberta, 2019.
Loff, Sarah. "New Viper Lunar Rover to Map Water Ice on the Moon." https://www.nasa.gov/feature/new-viper-lunar-rover-to-map-water-ice-on-the-moon.
Yiu, Tony. "Understanding Random forest: How the Algorithm Works and Why It Is So Effective." https://towardsdatascience.com/understanding-random-forest-58381e0602d2.