Productivity (m/h) was calculated as drilled meters divided by effective operating hours. 7.3 Regression Analysis and Elasticity Estimation The consolidated dataset was exported and processed in Python. A linear regression model was developed for each shift, using availability as the independent variable and utilization as the dependent variable. The regression slope coefficient (β) was used to calculate average operational elasticity for both day and night shifts. 7.4 Delay Analysis Delay causes were grouped and analyzed using Pareto diagrams to identify the main operational constraints affecting utilization. 8. RESULTS AND DISCUSSION 8.1 Operational Elasticity Results Regression results show negative operational elasticity in both day and night shifts. This indicates an inverse relationship between mechanical availability and utilization during drilling operations. The regression coefficients obtained for both shifts were statistically significant (Day shift: p = 0.042; Night shift: p = 0.040). This behavior suggests that operational limitations are preventing available equipment time from being effectively used in drilling operations. Figure 3 – Regression Results and Elasticity by Shift 8.2 Unproductive Time and Delay Distribution The delay analysis shows that a small number of causes represent the majority of unproductive time. Table 1 summarizes the main delay causes identified. Table 1 – Delay Time Distribution (hours) Delay Cause Day Shift Night Shift Total
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