Skilled Background: I’m Growth Geologist working in Cairn Oil and Gasoline Ltd (Vedanta Group) for one and half years. My work has been centered on Brown Subject initiatives in Offshore basins of India and I’ve been coping with an unlimited number of geological and geophysical datasets however I’ve at all times felt the necessity to analyze these knowledge utilizing machine studying algorithms and contribute to my group as a geo-data scientist. To date, my journey has been a fruitful one as I’ve used my learnings within the course to supply significant leads to my present job position.
Job position earlier than I joined the PGPDSBA Program: My position as Geologist is to grasp the subsurface rock properties and plan wells to be drilled in Hydrocarbon rock formations. Geosciences contain monumental uncertainties and my position includes contemplating a number of varieties of datasets reminiscent of geological, geochemical, geomechanical and geophysical. Total, my job is to plan wells utilizing a variety of datasets in order that drillers can penetrate Hydrocarbon targets in subsurface rock formations.
The issue that I confronted: In the course of the drilling marketing campaign, drillers want vital data like mud weight, formation lithology, anticipated strain in rock formations, geophysical anomalies and many others. My job is to conduct a prognosis of those variables and make a hypothetical geomechanical mannequin to grasp principal stresses that might be appearing throughout drilling. Wellbore stability points are a standard phenomenon through the drilling of various sections of a wellbore and so they have to be mitigated utilizing a calibrated geomechanical mannequin. My motivation was to prognose the mud weight window utilizing present drilling datasets in offset wells. Other than this, my job as a Geologist throughout real-time drilling operations is to interpret varied wireline log curves like gamma ray, resistivity and neutron porosity. I felt the necessity to interpret the hydrocarbon zones encountered utilizing novel knowledge visualization methods in Python. The method was tedious nevertheless it was definitely worth the effort.
The answer to the Geoscientific Drawback: I needed to construct a linear regression mannequin to foretell the mud weight home windows for quite a few drilling sections and thus I used a multi-variate regression mannequin for a similar. The next are the unbiased variables:
1. Weight on Bit (Lbs)
2. Price of penetration (ROP) m/hr
3. Rotations per minute (RPM)
4. Formation lithology (Categorical- transformed into numerical utilizing one-hot encoding)
5. Measured Depth (MD) m
6. True Vertical Depth Sub-sea (TVDSS) m
7. Complete Gasoline (%)
8. Gap Diameter (inches)
9. The inclination of the Borehole (levels)
10. The azimuth of the Borehole (Diploma)
11. Stand Pipe Strain (SPP)
12. Mud Movement price (USgal/min)
The goal variable was the drilling mud weight (ppg). Utilizing the stats mannequin, my outcomes have been pretty good and I acquired a mud weight window whereby, future wells might be deliberate. In your complete course of, I gathered vital statistical outcomes like Coefficient of Determinant, Adjusted R squared worth, Imply squared errors, Root of imply squared error and eventually acquired a linear equation with intercept and coefficient which expressed that mud weight was depending on a number of parameters talked about above and a linear expression was came upon to grasp this dependency.
Utility of this Deep studying Linear Regression methodology to the Drilling crew
This novel instrument might drastically scale back the mud weight uncertainty window in essential manufacturing sections within the borehole and would thus assist the drilling crew higher perceive the principal stresses concerned in order that good trajectory might be optimized for sure unstable lithological formations. Furthermore, the usage of Knowledge Analytics was extremely appreciated by the crew as drilling prices for sidetracked properly might be very costly and it is vitally vital to quantify these stresses in rock formations earlier than drilling the borehole.
Affect of the Machine studying train on the crew: Drilling crew and the subsurface crew was extremely happy with my work of enterprise the position of Knowledge analytics and machine studying to prognose essential geomechanical parameters of the deliberate properly trajectory of the borehole.
My key learnings: Other than this, it was a way of satisfaction for me as I efficiently used my learnings within the course and utilized them to an precise downside at hand. Drilling a borehole prices hundreds of thousands of {dollars} as there is no such thing as a room for error and it is vitally vital to prognose the mud weight home windows for each part of the wellbore that might be drilled for doable hydrocarbon accumulations. Nevertheless, there are quite a few geological uncertainties that are extraordinarily tough to mitigate as subsurface rock formations are fashioned in a wide range of depositional environments and the acquired geophysical knowledge solely tells part of the story. To conclude, I’m always studying a variety of machine studying algorithms on this course to unravel thrilling scientific and difficult enterprise issues within the Oil and Gasoline business.
