No abstract

Conventional production logging with electric line is sometimes challenged by the presence of mechanical restrictions in the wellbore. The fragility of production logging tools also impedes the use of electric-line coiled tubing (CT) with the risk of damaging tools across sections with little clearance. This study showcases conclusive flow profiling using distributed temperature sensing (DTS) via fiber optics deployed with CT in a gas condensate well where wellbore access prevented the use of logging tools. Flow profiling via DTS has been used globally in completions where fiber optic lines are permanently installed. Interpretation of those logs usually leverages months of acquired data to invert temperature information and obtain the evolution of flow distribution over time. The proposed methodology instead relies on hours of DTS acquisition through the temporary deployment of fiber optics with CT. A comprehensive sensitivity analysis on key unknown parameters is then performed using a fit-for-purpose thermal-flow simulator to match simulated and acquired temperature profiles, leading to a flow distribution of gas, condensate, and oil in the wellbore. Before the intervention, an evaluation study was run using a flow-thermal simulator to evaluate the expected sensitivity of wellbore temperature to poorly characterized downhole parameters, such as permeability, pressure, or skin. This allows determining the downhole conditions under which DTS is able to detect flow contribution for a specific candidate. During the operation, the CT equipped with fiber optics was stationed across production zones for a total of 06 hours. The data was processed and fed back to the simulator along with reservoir, well data, and surface rates. To further constrain data processing, pressure surveys were acquired during the CT run using a downhole gauge, both during flow and shut-in periods. Unknown reservoir properties were sensitized during data interpretation to obtain a match between acquired DTS profiles and simulated wellbore temperature evolution, which, in turn, yielded an associated flow distribution. The matching exercise being an open-ended mathematical problem, several scenarios were considered, and their results checked against further production characterization of the wellbore and the field. The proposed case study illustrates how this methodology enabled logging in a mechanically-restricted zone and helped determining that the top interval was not contributing to flow. Flow profiling can be performed using a wide range of complementary logging tools, but the evolution of completions over the past few years is increasingly introducing mechanical restrictions that prevent the conveyance of such tools altogether. This study demonstrates that DTS can be a viable alternative for assessing zonal flow contributions. It also discusses the conditions under which this methodology is achievable.

No abstract

Lack of real-time downhole data for accurate depth correlation and precise control of pressure actuated tools, often result in inefficient coiled tubing (CT) interventions. Surface readouts have been conventionally used to infer downhole conditions during CT operations; however, the presence of the above-mentioned unknowns along with dynamic wellbore conditions make surface measurements an insufficient approach for knowledge of the actual downhole conditions. This study describes how access to real-time downhole measurements was gained by using CT fiber-optic downhole telemetry and how its implementation contributed to address operational challenges encountered during CT abrasive perforating interventions in Pakistan. The application of CT equipped with fiber optics and instrumented bottom hole assembly (BHA) to vertical wells in onshore Pakistan required specific designs and new processes for preparing, executing, and evaluating well interventions. Planning and design considerations included selecting the BHA and performing pre-job quality checks of the optic fiber. This novel approach leveraging fiber optic telemetry relies on fibers inside an inconel tube within a CT string, and a downhole BHA that includes pressure and temperature gauges and a casing collar locator (CCL). The BHA acquires real-time data providing quantitative feedback of downhole wellbore conditions during the interventions, which enables accurate placement and controlled actuation of the hydraulic abrasive perforating gun. Depth accuracy for tool positioning, and differential pressure across the gun nozzles were of utmost importance for suitable abrasive perforating interventions. Downhole pressure gauges monitored the annulus between CT and production liner, and CT internal pressures at all times, helping to keep the differential pressure within the 2200 – 2600 psi for optimum abrasive perforating. The CCL data was utilized to correlate depth for precise perforations placement. Multiple wells were perforated using the combination of CT fiber-optic telemetry and abrasive perforating. The BHA delivered real-time downhole data, which helped to understand the changing wellbore conditions. Implementation of this new methodology increased the operator’s confidence with abrasive perforating, as previously very little downhole data was available to make informed decisions to optimize such interventions and ensure effective perforating at target depth. This study introduces a novel perforating technique in Pakistan. The use of CT fiber-optic downhole telemetry is not limited to perforating, and the BHA can also acquire gamma ray, tension and compression forces, torque, and even flow data. Such systems can have a significant impact in overcoming intervention challenges faced today in Pakistan.

Fused deposition modeling (FDM) is a popular 3D printing technique that creates parts by heating, extruding, and depositing filaments made of thermoplastic polymers. The processing parameters have a considerable impact on the characteristics of FDM-produced parts. This paper focuses on the parametric prediction of the FDM process to predict ultimate tensile strength and determine a mathematical model using the Taguchi method and Artificial Neural Network. Five manufacturing variables, such as layer thickness, print speed, orientation angle, number of parameters, and nozzle temperature at five levels, are used to study the mechanical properties of PLA material to manufacture specimens using FDM 3D printer. The specimens are produced for tensile tests in accordance with ASTM-D638 standards, and the process parameters are established using the Taguchi orthogonal array experimental design technique. The results proved that the printing process parameters significantly impacted the tensile strength by changing the tensile test values between 37 MPa and 53 MPa. Also, the neural network predicted the tensile strength values, and the maximum error was equal to 8.91%, while the mathematical model had a maximum error equal to 19.96%.