Browsing by Author "Etaje, Darlington Christian"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Development of a Drilling Simulator to Achieve Drilling Optimization
    (2023-08) Etaje, Darlington Christian; Roman, Shor; Gates, Ian; Chen, Nancy; Park, Simon; Azadbakht, Saman
    In summary, drilling simulation, a set of physic-based models run through time or depth steps to mirror events in the drilling rig, is the backbone of all field testing of technologies or procedures. If a model has been validated using drilling simulation, the risk of wasted field trial is lowered significantly. This is why the formulation of models that make up drilling simulation is key and this is what this thesis has focused on. 20 functions were used to simulate the processes described in this research. Finite element formulation of space models linked with time-based models have been developed for the 2-node system in X (axial loading and axial torsion), Y (transverse bending of Z), and Z (transverse bending of Y) directions. Laplace transform was used to solve the time based partial differential equation paving way for the development of velocity, acceleration, force, and torque equations. Drill ahead modeling using build and walk relation to resultant forces was validated. Stick slip mitigation using the optimized RPM objective function was used to optimize the mechanical efficiency of drilling. Particle swarm optimization was the process used for optimization where each solution is considered a particle in search of the global minimum. An expression of the optimized RPM was developed and simulated with field data. Confined compressive strength of the field data was compared with the CCS obtained from the simulation but there was no perfect match yet. Further runs of the simulation would show more lessons as to how to improve the results. It can be concluded that the MSE minimization process should rather be called MSE optimization process as the decision to raise or lower MSE should be based on the data supplied to the particle swarm optimizer since the objective function is built with constraints to lower drill string vibrations. When tested with field data, the objective function and optimizer built in this research was found to increase MSE but lower the downhole stick slip index by 28 percent. The downhole stick slip index was below 0.5.
  • Thumbnail Image
    ItemOpen Access
    Identifying the Optimum Zone for Reducing Drill String Vibrations
    (2018-07-10) Etaje, Darlington Christian; Shor, Roman J.; Gates, Ian Donald; Hassay, Derek N.
    This thesis was written to address the vibration problems that occur during drilling operations. Due to the rotational motion effected on the drill string while drilling, vibrations occur, and when these vibrations become excessive, the drill string may oscillate in a manner that could damage the pipes and damage other tools attached to the drill string. Machine learning may be used to identify the vibration prone zones and provide recommendations to the driller to change the operating weight on bit (WOB) and rotation speed (RPM) to achieve drilling efficiency while reducing the possibility of damages downhole. Data received from the rig is processed through a dimension reduction process and then categorized using a decision tree classification method. The rules behind the decision tree was created by reversing conventional ways of curbing vibration problems during drilling operations. In the course of the research, it was discovered that there is a need for additional safety gap away from the usual boundary for vibration problems. Quantitative risk analysis was used to identify this gap. This report explains the process of identifying that safety gap. The machine learning model used throughout this research was trained on recorded downhole data and tested with surface data from the electronic drilling recorder. The reports also highlight the findings from market research done to identify the possibility of deploying this research as a startup in Calgary, Canada. Detailed competitor analysis is shown based on customer discovery and customer validation interviews. This has led to the development of business model canvas which is described in this report. A blue ocean strategy was graphed showing that the startup, “Optimum Zone Identifier, OZI” can be differentiated from competitors by being in a market segment that has unique needs with OZI being the only player fitting this category.