Professional Background: I’m Development Geologist working in Cairn Oil and Gas Ltd (Vedanta Group) for one and half years. My work has been targeted on Brown Field tasks 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. So far, my journey has been a fruitful one as I’ve used my learnings within the course to provide significant leads to my current job function.
Job function earlier than I joined the PGPDSBA Program: My function as Geologist is to know the subsurface rock properties and plan wells to be drilled in Hydrocarbon rock formations. Geosciences contain huge uncertainties and my function includes contemplating a number of sorts of datasets equivalent to geological, geochemical, geomechanical and geophysical. Overall, my job is to plan wells utilizing a variety of datasets in order that drillers can penetrate Hydrocarbon targets in subsurface rock formations.
The drawback that I confronted: During the drilling marketing campaign, drillers want essential info like mud weight, formation lithology, anticipated stress 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 understand principal stresses that will be performing throughout drilling. Wellbore stability points are a typical phenomenon through the drilling of various sections of a wellbore they usually should be mitigated utilizing a calibrated geomechanical mannequin. My motivation was to prognose the mud weight window utilizing current drilling datasets in offset wells. Apart from 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 course of was tedious however it was well worth the effort.
The answer to the Geoscientific Problem: I wished 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 following are the impartial variables:
1. Weight on Bit (Lbs)
2. Rate 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. Total Gas (%)
8. Hole Diameter (inches)
9. The inclination of the Borehole (levels)
10. The azimuth of the Borehole (Degree)
11. Stand Pipe Pressure (SPP)
12. Mud Flow price (USgal/min)
The goal variable was the drilling mud weight (ppg). Using 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 essential statistical outcomes like Coefficient of Determinant, Adjusted R squared worth, Mean squared errors, Root of imply squared error and at last 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 discovered to know this dependency.
Application of this Deep studying Linear Regression methodology to the Drilling group
This novel instrument may drastically scale back the mud weight uncertainty window in crucial manufacturing sections within the borehole and would thus assist the drilling group higher perceive the principal stresses concerned in order that good trajectory might be optimized for sure unstable lithological formations. Moreover, the usage of Data Analytics was extremely appreciated by the group as drilling prices for sidetracked nicely might be very costly and it is rather essential to quantify these stresses in rock formations earlier than drilling the borehole.
Impact of the Machine studying train on the group: Drilling group and the subsurface group was extremely happy with my work of endeavor the function of Data analytics and machine studying to prognose crucial geomechanical parameters of the deliberate nicely trajectory of the borehole.
My key learnings: Apart from this, it was a way of satisfaction for me as I efficiently used my learnings within the course and utilized them to an precise drawback at hand. Drilling a borehole prices tens of millions of {dollars} as there isn’t a room for error and it is rather essential to prognose the mud weight home windows for each part of the wellbore that will be drilled for doable hydrocarbon accumulations. However, there are quite a few geological uncertainties that are extraordinarily tough to mitigate as subsurface rock formations are shaped in a wide range of depositional environments and the acquired geophysical knowledge solely tells part of the story. To conclude, I’m continuously studying quite a lot of machine studying algorithms on this course to resolve thrilling scientific and difficult enterprise issues within the Oil and Gas trade.