Simulation Model to Optimize Drilling Parameters for Proper Hole Inclination in Horizontal Wells
- 1. A SIMULATION MODEL TO OPTIMIZE DRILLING PARAMETERS FOR PROPER HOLE INCLINATION IN HORIZONTAL WELLS PAUL OBINNA OKAFOR
- 2. Contributions to Knowledge/Conclusion Simulation Model (OPTIDRILL) Introduction Statement of Problem Aim / Objectives Literature Review
- 3. INTRODUCTION Drilling a well has been proven to be the only known way to recover hydrocarbon fluids from the reservoir, it simply is the process of creating a hole. An inclined well is a well that is drilled at an angle to the vertical. A well is termed “horizontal” if it makes an angle of at least 80° with the vertical
- 4. INTRODUCTION WHY DRILL DEVIATED WELLS Side Tracking Drilling below a Populated Area Maximum Reservoir ContactDrilling below a Surface Obstruction Source: IFP Training Material
- 5. Drilling optimization is the process of analyzing effects and collaborations of drilling variables through mathematical modeling to attain maximum efficiency of the drilling operation. Optimizing various drilling parameters can help reduce cost and time of drilling operations, increase performance and reduce the probability of occurrence of hole problems, hence, maximizing profit INTRODUCTION
- 6. INTRODUCTION Controllable Parameters Uncontrollable Parameters Mud Formation to be Drilled Hydraulics Depth to be drilled Bit Type and tooth wear Rock Properties Rotational Speed (RPM) Bottom hole Mud Pressure Weight on Bit Overbalance Mud Pressure
- 7. STATEMENT OF PROBLEM Major challenges faced while drilling Horizontal wells include; i) Small Bit weight. ii) Trajectory Control iii) Difficulty in Hole cleaning iv) Higher Torque and Drag These complications can be avoided or greatly reduced by proper optimization of Drilling Parameters
- 8. AIM AND OBJECTIVES AIM The aim of this study is to design a model that can optimize drilling parameters with Polynomial Regression Algorithm using known drilling data Collect and Validate Field Data, Determine from the data, the parameters with the most effect on Drilling rate using Multiple Regression technique in MS Excel Design the Simulation Model with the Polynomial Regression Algorithm Design the Simulation Model to calculate Drilling Cost ($/ft.) using the optimized Drilling Rate.
- 9. LITERATURE REVIEW Bourgoyne and Young in 1973 built a unique model using Multiple Regression Analysis from known field data. In their approach, they assumed that various other parameters were required to effectively understand and optimize the rate of penetration. The other parameters they included were; Depth, Formation Strength, Compaction, Bottom hole Pressure Differential, Bit Size, Weight on Bit Rotational Speed (RPM) Hydraulics Bit tooth wear.
- 12. SIMULATION MODEL Polynomial Regression Polynomial equation in m degree may be taken as : y = a0 + a1x + a2x2 +....amxm+ e Here a0 , a1, ……. am are constant and e = residual error
- 13. Horizontal Drilling Optimization Model Collect and Validate Field Data Determine Impact of Parameters on Rate of Penetration using Multiple Regression Analysis Discard Insignificant Drilling Parameters Use Parameters with Huge Impact on ROP Design Computer Program with Polynomial Regression Algorithm Design Computer Program to Import Field Data with the Analyzed Parameters, fit the Polynomial Regression Algorithm and calculate the trend Design the Computer Program with Drilling Parameters as the Input Variables Optimize the Imported Parameters to achieve Best ROP by inputting new values of Drilling Parameters Model Flow Chart
- 14. SIMULATION MODEL Multiple Regression Analysis of REFERENCE DATA Multiple R: 0.89277445 R Square: 0.79704621 Adjusted R Square: 0.77449570 Standard Error: 30.5462339
- 16. DRILLING COST ANALYSIS Drilling Optimization Triangle
- 17. CONCLUSION Linear Regression Model Polynomial Regression Model
- 18. CONTRIBUTIONS TO KNOWLEDGE Designed and Built a Computer Program to optimize drilling parameters for proper hole inclination in horizontal drilling” I improved the Bourgoyne and Young ROP model by using a more improved big data analysis technique which is the POLYNOMIAL REGRESSION, while still considering some of the drilling parameters considered by Bourgoyne and Young. I introduced “Hole Cleaning” as a new Variable, which is necessary to estimate how much cuttings is being removed from the wellbore. I included a “Drilling Cost” calculator in the computer program; this will be used to calculate the Cost savings of the optimized drilling rate.