Figuring out Key Stress Variables Earlier than Drilling in Geoscientific Setting



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 infinite number of geological and geophysical datasets however I’ve all the time felt the necessity to analyze these information utilizing machine studying algorithms and contribute to my group as a geo-data scientist. Up to now, my journey has been a fruitful one as I’ve used my learnings within the course to supply significant ends in 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 monumental uncertainties and my function entails contemplating a number of kinds of datasets equivalent to geological, geochemical, geomechanical and geophysical. General, 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: Throughout 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 might be appearing throughout drilling. Wellbore stability points are a standard phenomenon throughout 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. 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 information visualization methods in Python. The method was tedious but it surely was well worth the effort.

The answer to the Geoscientific Downside: 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 next are the impartial variables:

1. Weight on Bit (Lbs)

2. Charge 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 Move charge (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 could be deliberate. In the complete course of, I gathered essential 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 know this dependency.

Software of this Deep studying Linear Regression technique to the Drilling group

This novel instrument might drastically cut back the mud weight uncertainty window in essential manufacturing sections within the borehole and would thus assist the drilling group higher perceive the principal stresses concerned in order that good trajectory could be optimized for sure unstable lithological formations. Furthermore, the usage of Information Analytics was extremely appreciated by the group as drilling prices for sidetracked effectively could be very costly and it is vitally essential to quantify these stresses in rock formations earlier than drilling the borehole.

Influence 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 Information analytics and machine studying to prognose essential geomechanical parameters of the deliberate effectively 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 thousands and thousands of {dollars} as there isn’t a room for error and it is vitally essential to prognose the mud weight home windows for each part of the wellbore that might be drilled for potential hydrocarbon accumulations. Nonetheless, there are quite a few geological uncertainties that are extraordinarily troublesome to mitigate as subsurface rock formations are shaped in a wide range of depositional environments and the acquired geophysical information solely tells part of the story. To conclude, I’m continuously studying plenty of machine studying algorithms on this course to resolve thrilling scientific and difficult enterprise issues within the Oil and Gasoline trade.


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