A New Automated Cutting Volumes Model Enhances Drilling Efficiency

Cuttings concentration in annulus that are generated while drilling due to the rate of penetration have several problems if it is high and has exceeded the maximum limit. Cuttings accumulation in annulus can lead to hole problems such as lost circulation coincident, stuck pipe incidents, and slow drilling rate which still a difficult challenge once it comes to plan, design and drill wells. If a proper hole cleaning efficiency can be achieved, that ultimately will enable the drilling team to have satisfied well drilling performance. To empower hole-cleaning performance, it must be engineered. In this paper, a new real time model of cuttings concentration in annulus (CCA) enhances drilling performance that will ensure optimized improvement and avoid stuck pipe problems. Knowledge from this paper will help in modeling and monitoring cuttings concentration in annulus while drilling precisely and therefore facilitate improving ROP without jeopardizing the well drilling performance. In addition, the paper spoke about the environment impact, drilling waste management and economics and wellbore instability related to cuttings concentration in annulus. Huge number of models, techniques, charts, chemicals, tools, methods, and designs, experimental studies and to enhance the hole cleaning, but these things are just based on theory, lack proper experimental data, not compatible with drilling scenarios and operations. Cutting Concentration in Annulus (CCA) can provide a clue or a knowledge for maximum ROP that is save and compatible with rig equipment limitations such as the knowledge about maximum amount of cuttings generated while drilling that can be transported to surface and that the shale shakers can handle without causing hole troubles. The size of hole sections (OH), ROP, flow rate of mud pump (GPM), annular Velocity, Critical velocity (Vc), Drill pipe size (OD), cuttings rise velocity (Vcr), slip cuttings velocity (Vsc) and transport ratio (TR) properties in certain hole sections were collected and analyzed first to determine the effect of them on hole cleaning and ROP performance. The data selected are from the same hole size, formation type and mud type. The relationships between the collected properties and CCA were t evaluated to determine how strong it is and demonstrate the significance of properties on estimating CCA. CCA was monitored controlled and evaluated to be able to have improved hole cleaning performance to enhancement of the drilling operations and decrease well drilling time. This is the first time to for hole cleaning optimization to lead for ROP improvement by using real time CCA model. The developed model has been validated using field data during drilling hole sections. It has shown high drilling rate performance in the hole sections tested and helped mitigate stuck pipe incidents, increased ROP by more than 52%.