A stiff-string torque & drag & buckling model has been coupled to a 3D meshed casing wear calculation to model the effect of drilling operations on casing wall thickness reduction. Results of the numeric simulation are compared to MFCL for field cases and provide an understanding of possible well integrity issues during the well life cycle. A Hall wear model and 3D rigid stiff string model is used to predict casing wear. Using Wear Factor Coefficients obtained for several hard-banding, casing grade and mud-types, the process was tested against numerous field cases. For each field case, a history of drill string runs were modelled and filtered into operations in which the drill string rotates such as Drilling, Reaming, Back reaming and ROB. Initially, the calliper log was not analysed to ensure the results are non-biased using experimentally found Wear Factors. In a second phase, the Wear Factors were calibrated against the measured calliper log. This paper provides a methodology that was used to successfully quantify the effect of casing wear against numerous field cases. The 3D orientation of the casing wear was found from an un-calibrated MFCL. After calibration of the new Wear Factor, the casing wear can be predicted before and after well construction. When used as a post analysis tool, the methodology helps determine if casing wear was a root cause of loss of well integrity. This process also helps reduce the uncertainty in Wear factor as a major unknow in the contact force, is now properly modelled. When used in a post analysis process, the results where quantitative (groove depth vs 3D orientation) but also qualitative, providing a post-mortem description of the state of the well to operation's personnel. The methodology presented here can be used to both predict excessive casing wear and determine if excessing casing wear was a cause of failure. It can be used to help determine the state of well for workover operations or plug and abandonment operations. Accurate casing wear prediction at planning stage allows the anticipation of costly but fit for purpose mitigation means & measures.
This paper describes the innovative engineering workflow which has been used to ensure the safe deployment of deep production liners on long step-out wells of a deep offshore development field. It highlights the importance of accurate Torque & Drag modelling during planning and operations and provides details on how the use of downhole data assisted in understanding downhole conditions on the first wells, which allowed to optimize the running and setting procedure for the next wells of the field. For this methodology, a unique Torque & Drag stiff-string model was used to simulate the evolution of side-forces, tension, stretch, torque and twist along the string at every stage of the deployment and setting of the liner. Simulations were performed both during planning phase and operations. Once the well completed, downhole memory data from a logging tool was compared with simulations, which allowed to calibrate the model, better understand downhole conditions, and provide recommendations for the next runs. Using this methodology, the operator succeeded in deploying the liner to total depth, setting the hanger and packer successfully on all the wells of the field. These operations were performed with only 40 minutes of non-productive time throughout the campaign. The paper shows how correlating downhole data with Torque & Drag simulations highlighted areas of improvement and allowed to optimize the running and setting procedure of the liner. It also led the operator to gain confidence in the feasibility of such critical operations even on the more challenging wells. Detailed engineering and collaboration were key to this success. Such methodology can be applied on every well where weight transfer is a potential issue. As the industry is heading towards digitalization and automation, this case study is a prime example which demonstrates the added value of combining advanced physics-based simulations with time based downhole data.
Objectives/Scope This paper will present predicted vs. measured wear for six wells that were analysed in the Culzean field, which is a high-pressure, high-temperature (HPHT) gas condensate field located in the central North Sea. The focus rests on the casing wear prediction, monitoring and analysing process and within that, especially on how to make use of offset data to improve the accuracy of casing wear predictions. Methods The three major inputs to successfully predict casing wear are: Trajectory & Tortuosity, Wear Factor and required rotating operations. All those were calibrated based on field measurements (High-resolution gyro, MFCL (Multi-Finger-Caliper-Log) and automatically recorded rig mechanics data), to improve the prediction quality for the next section and/or well. The simulations were done using an advanced stiff-string model featuring a 3D mesh that distinguishes the influence of different contact type and geometry on the wear groove shape. The "single MFCL interpretation method", in which the wear is measured against the most probable elliptical casing shape and herby allowing wear interpretation with only one MFCL log and avoiding bias error, was applied. (Aichinger, 2016) Results, Observations, Conclusions For the six wells that were analysed the prediction of the largest wear peak per well section was compared to the measurement. In the planning phase (before any survey data was available) the mean error on the wear groove depth was +/− 0.025 [in] (+/− 0.6 [mm]), the maximum error was +/− 0.045 [in] (1.1 [mm]). The average error of the results is summarized in Figure 10 and laid out in detail in Figure 9. Generally, the predictions are accurate enough to be able to manage casing wear effectively. In this particular case, the maximum allowable wear on the intermediate casing was extremely limited to ensure proper well integrity in case of a well full of gas event while drilling an HTHP reservoir. Novel/Additive Information This paper should provide help to Engineers who seek to improve the accuracy of casing wear prediction and hence improve casing wear management. It presents a new way of anticipating tortuosity based on offset well data and it offers a suggestion on how to deal with MFCL measurement error during Wear Factor calibration and Wear prediction.
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