Hole cleaning performance monitoring during the drilling of directional wells

During drilling operation, cuttings are produced downhole and must be removed to avoid issues which can lead to Non Productive Time (NPT). Most of stuck pipe and then Bottom-Hole Assembly (BHA) lost events are hole cleaned related. There are many parameters which help determine hole cleaning conditions, but a proper selection of the key parameters will facilitate monitoring hole cleaning conditions and interventions. The aim of Hole Cleaning Monitoring is to keep track of borehole conditions including hole cleaning efficiency and wellbore stability issues during drilling operations. Adequate hole cleaning is the one of the main concerns in the underbalanced drilling operations especially for directional and horizontal wells. This dissertation addresses some hole cleaning fundamentals which will act as the basis for recommendation practice during drilling operations. Understand how parameters such as Flowrate, Rotation per Minute (RPM), Rate of Penetration (ROP) and Mud Weight are useful to improve the hole cleaning performance and how Equivalent Circulate Density (ECD), Torque & Drag (T&D) and Cuttings Volumes coming from downhole help to indicate how clean and stable the well is. For case study, hole cleaning performance or cuttings volume removal monitoring, will be based on real-time measurements of the cuttings volume removal from downhole at certain time, taking into account Flowrate, RPM, ROP and Drilling fluid or Mud properties, and then will be plotted and compared to the volume being drilled expected. ECD monitoring will dictate hole stability conditions and T&D and Cuttings Volume coming from downhole monitoring will dictate how clean the well is. T&D Modeling Software provide theoretical calculated T&D trends which will be plotted and compared to the real-time measurements. It will use the measured hookloads to perform a back-calculation of friction factors along the wellbore.

A SDK improvement towards gesture support

Human-Computer Interaction have been one of the main focus of the technological community, specially the Natural User Interfaces (NUI) field of research as, since the launch of the Kinect Sensor, the goal to achieve fully natural interfaces just got a lot closer to reality. Taking advantage of this conditions the following research work proposes to compute the hand skeleton in order to recognize Sign Language Shapes. The proposed solution uses the Kinect Sensor to achieve a good segmentation and image analysis algorithms to extend the skeleton from the extraction of high-level features. In order to recognize complex hand shapes the current research work proposes the redefinition of the hand contour making it immutable to translation, rotation and scaling operations, and a set of tools to achieve a good recognition. The validation of the proposed solution extended the Kinects Software Development Kit to allow the developer to access the new set of inferred points and created a template-matching based platform that uses the contour to define the hand shape, this prototype was tested in a set of predefined conditions and showed to have a good success ration and has proven to be eligible for real-time scenarios.