The economic success of many drilling operations depends on the availability and reliability of real-time information about the drilling process. Mud pulse telemetry is currently the most common method of transmitting measurement-while-drilling (MWD) and logging-while-drilling (LWD) data. Advances in downhole sensing for drilling optimization and formation evaluation are placing heavy demands on telemetry systems to provide fast and reliable data rates from greater depths. However, solid particle erosion poses a significant problem for telemetry tools, where solid particle (such as sand) impingement could damage the tool string and shorten the service life of the tools. Therefore, a comprehensive investigation on erosion of mud pulse telemetry tools consisting of numerical simulation and field tests is often required to optimize the tool design. In the field, many factors can influence telemetry tool erosion such as material properties, sand size, geometry, flow velocity, operating pressure, and turbulence. These factors interact with each other, making the experimental study of all influencing parameters very challenging and time-consuming. In this work, computational fluid dynamics (CFD) simulations were used to study the effect of several parameters on the erosion rate, even in complex geometries where setting up an experimental study is difficult. The erosion rate was determined using the widely used Oka erosion model. Parameter studies were then performed to find the influence of flow rate and sand concentration on the erosion rate. Simulation was also performed to support the deployment of new engineered materials. For model validation, simulation results were compared with erosion patterns from field tests, showing good agreement between field observations and simulation results. Based on findings from the parameter studies, a formula of key performance indication (KPI) parameter was developed to evaluate the erosion performance of the mud pulse telemetry tools deployed in the field. After completing the field experiments, 3D laser scans of the deployed tools with different materials were performed. In addition, KPI values were calculated based on the scanning results to evaluate the actual erosion performance. Evaluation revealed that the new engineered alloy was eight times more erosion-resistant than stainless steel, which was consistent with the CFD simulation results. The results of this study indicate that CFD simulation provided an alternate way to predict solid particle erosion on logging tools in downhole environments. By using the high-fidelity erosion model, the tool erosion rate could be accurately predicted. Based on this conclusion, the erosion risk can be mitigated by providing guidance on repair and maintenance intervals and planning the drilling process to avoid premature tool failures. This approach will eventually improve the reliability and safety of downhole tool and reduce non-productive time (NPT) and costs.

As operators strive to become more efficient on cost and performance during well construction, their approach to tracking invisible lost time (ILT) and non-productive time (NPT) has been revamped. A state-of-the-art software platform has been introduced into the market to monitor and optimise drilling performance in real-time, enabling early detection of ILT and NPT events, ultimately saving rig time and reducing associated costs. Given the current economic climate, this platform allows for a smart approach to reduce inefficiencies when drilling a well by its innovative approach of benchmarking and monitoring. One of the competitive advantages of utilising this platform for a drilling advisory service is the pre-job study, in conjunction with the operator, to develop the key performance indicators (KPI) that will be referenced as benchmarks for real-time trend monitoring. Up to 39 different KPI's, such as rate of penetration, on and off-bottom time, circulating and connection times are determined, however for this study, the performance indicator of connection times while drilling and tripping will be analyzed. With a more user-friendly interface, optimized for 24/7 trend monitoring, this platform serves to be a more beneficial and market-leading approach to performance monitoring. The aforementioned, coupled with breakthrough thinking, reveals the potential for savings which can be harvested by the operator by driving operational performance, resulting in more proficient operations. This paper will showcase one such case history to reveal missed opportunities and the associated cost and value benefits.

Downhole fluid samples enable better quantification of condensate-to-gas ratios that are required for effective reservoir estimation and forecasting. Quality of samples directly affect the measured properties, i.e., fluid compressibility and viscosity, that provide the supplemental information necessary for planning prospective wells through improved understanding of the reservoir.An industry-leading, logging-while-drilling (LWD) fluid analysis and sampling tool was successfully deployed on 21 jobs with 46 runs to date, and completed 400 pressure tests with 109 samples recovered worldwide in shelf and deepwater projects. This paper highlights a new systems application for this technology to acquire single-phase fluid samples for a major operator in Trinidad. The acquired data was used for early investigation of reservoir connectivity.The technology was introduced to the oil and gas industry in 2011. High sample quality, testing without sticking, and sample acquisition in complex well geometries and designs differentiates this technology for fluid analysis and sampling from existing wireline alternatives. Benefits of the LWD method include improved cleanup time due to reduced invasion, reduced overall sample acquisition costs, and reduced risk of stuck pipe. This method enables sampling in extended-reach wells and enables accurate fluid identification of oil, gas or water. This paper describes the learning and the innovative systems methodology employed to successfully attain information pertinent to evaluating and understanding reservoirs. LWD Fluid Analysis Sampling and Testing ServiceLWD formation testing and sampling will be an important asset to the oil and gas industry for many decades. The vital information provided by this service is used throughout the life cycle of a reservoir; but more important is the data's influence on the initial assessment of the commercial potential of a project. This assessment includes estimates for producibility, fluid type and composition, fluid phase behavior, production facility design and flow assurance. These estimates are critical to the long-term success of a project because subsequent intervention or redesign could affect the project's economic viability.The capabilities and efficiencies of LWD formation testing tools are dramatically changing the value of formation sampling and testing programs. It is now possible to obtain pressure measurements and

Downhole fluid samples enable better quantification of condensate-to-gas ratios that are required for effective reservoir estimation and forecasting. Quality of samples directly affect the measured properties, i.e., fluid compressibility and viscosity, that provide the supplemental information necessary for planning prospective wells through improved understanding of the reservoir. An industry-leading, logging-while-drilling (LWD) fluid analysis and sampling tool was successfully deployed on 21 jobs with 46 runs to date, and completed 400 pressure tests with 109 samples recovered worldwide in shelf and deepwater projects. This paper highlights a new systems application for this technology to acquire single-phase fluid samples for a major operator in Trinidad. The acquired data was used for early investigation of reservoir connectivity. The technology was introduced to the oil and gas industry in 2011. High sample quality, testing without sticking, and sample acquisition in complex well geometries and designs differentiates this technology for fluid analysis and sampling from existing wireline alternatives. Benefits of the LWD method include improved cleanup time due to reduced invasion, reduced overall sample acquisition costs, and reduced risk of stuck pipe. This method enables sampling in extended-reach wells and enables accurate fluid identification of oil, gas or water. This paper describes the learning and the innovative systems methodology employed to successfully attain information pertinent to evaluating and understanding reservoirs.