Efficient cuttings transport and hole cleaning is a very important factor that must be considered during drilling operations. In inclined and horizontal drilling, hole cleaning is a common issue since there is high tendency for formation of cuttings bed in the hole which can lead to several complex problems. The optimization of cuttings transport depends on so many factors like hole angle, cutting size, drill string rotation, drill pipe eccentricity, bit hydraulics etc. This paper is designed to examine critical factors that affect the efficient cleaning/transport of cuttings and bit hydraulics in inclined wells with a view to understanding how to minimize drilling difficulties thereby reducing non producing time (NPT) during drilling operations. The developed model ensures proper hole cleaning in the critical hole angle (between 45° and 60°) as well as horizontal wells and determining the optimum flowrate and rate of penetration (ROP) that will ensure successful drilling. The model also helps to save time wasted when we encounter problems of high concentrations of cuttings which causes high Equivalent circulation density (ECD) that can result in lost circulation problem amongst others. Finally, the developed model is validated using field data and graphs showing how the penetration rate increases as cuttings concentration in the annulus reduces thereby optimizing drilling operations.

There are numerous problems encountered during drilling such as wellbore instability, drilling mud weight estimation, as well as selecting good casing and bit for the drilling operations. It is therefore important to understand and accurately determine the strength of the rock in order to avoid these common drilling problems which are mostly encountered during well operations. It is of paramount importance to determine uniaxial compressive strength (UCS) from core and sonic log data so as to accurately predict rock strength for better well planning. In this work, we were able to obtain a correlation to determine UCS from data obtained from ten (10) wells in different locations in onshore Niger Delta using the regression analysis method. The correlation of UCS versus Poisson's ratio gave R2 value of 90.0%. The R2- value tending towards one (1) indicates that this model can be reliably used to predict ND-UCS and the p<0.05 shows that there is significant relationship between ND-UCS and Poisson's ratio. The model was validated with an entirely different well data and it predicted over 89% rock UCS data when compared to the actual rock UCS data. This study also provides an understanding of the variation in UCS and Poisson's ratio with depth for effective rock property analysis and evaluation. These correlations will help well engineers to make informed decisions on rock strength predictions during well planning and operations as well as manage wellbore stability optimally.

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