In this research lab and field corrosion coupon testing was completed to determine corrosion rates on commonly used Steam Assisted Gravity Drainage (SAGD) metals. This was done to evaluate general corrosion rates, and how they vary with well depth, and operating environment. Based on this analysis and evaluating scale composition a dominating corrosion mechanism was also determined. Lab testing utilized high temperature high pressure autoclaves to allow for field various corrosion rate and characterization measurements to be taken under controlled conditions. Measurements taken include linear polarization resistance, weight loss, and zero resistance annode to determine corrosion rates. Cyclic potentiodynamic polarization and potentiostatic polarization measurements were also obtained to further evaluate a materials effectiveness in preventing pitting corrosion. Materials tested in the lab were 1018 carbon steel, Deloro-40 and Stellite-6 hard facing finishes, TN-55TH, galvanized (GLV) J-55, and K-55. Field coupon testing consisted of installing corrosion coupons at various elevations in the annulus of a producting SAGD well. Coupon materials tested included L-80, J-55, and a GLV-J55+J-55 creviced couple. Analysis consisted of weight loss corrosion rate determination, visual inspections, and x-ray diffraction analysis to determine scale compositions. Field coupons showed corrosion rates decreasing from 0.0178mm/y at the bottom of the well to 0.0145mm/y at higher well elevations. This corresponded to a decrease in iron sulfide (FeS) scale content from the well bottom upwards. Based on the scales composition, operating conditions, and fluids present, it is likely that the scales were formed through the well known solid-state reaction between aqueous H2S and the metal. High average corrosion rates of 0.263mm/y were measured in the lab, compared to a low 0.0183mm/y in field studies. This difference is due to the inhibiting effects of oil in the field which inhibits corrosion rates and the longer field test duration.
The operator experienced an unusual casing failure at a producing SAGD (steam assisted gravity drainage) oil well in summer of 2017. The subject well in the Firebag SAGD field of NE Alberta, Canada had operated successfully for over 11 years. Once the problem was identified, the well was shut in to determine the nature of the failure and options for repair and recovery so it could be returned to operation as soon as possible. Tasks included identifying and isolating the failure, establishing the cause and nature of the failure, and determining viable repair options. Logging diagnostics to measure/image the failure were performed, which included new ultra-sonic logging imaging technology, high-resolution multi-finger caliper logging, a downhole camera run and conventional eddy flux casing inspection log. Historical log data was also reviewed to assess whether the failure evolved over time, or if the mechanism was acute. Once the nature of the failure was established, the optimal repair method was chosen, planned and carried out. Sophisticated analysis of multi-finger caliper log data, camera images and new technology in the form of an ultrasonic imaging tool for the casing were utilized and are presented. A discussion of potential root cause mechanisms for thermal wells is provided, including a variety of failure modes that could be ruled out. Confidence in the failure mode specific to this well was increased by considering information acquired from multiple diagnostic tools. The nature of the connection failure determined from this process is outlined, along the rationale behind the repair method selected to remediate the well.
High-resolution acoustic imaging technology provides operators the ability to extract submillimetric measurements of perforations at any depth into the casing wall. Due to its three-dimensional nature, submillimetric acoustic data permits the extraction of highly accurate area-based measurements at any radial distance into the perforation, with key distances at the inner and outer casing boundary. This novel technology is fluid agnostic and is unaffected by fluid opacity or clarity. The platforms robust 3D measurement capabilities have made it into an ideal means to evaluate casing and perforations in challenging environments such as hydraulically fractured wells. The integration of high-resolution acoustic imaging into numerous operators’ hydraulic fracture and completions evaluation workflows has resulted in a highly insightful aggregate submillimetric perforation dataset. This large dataset has led to the development of a method to virtually unplug perforations by using a well-specific "perforation entry and exit-hole area correlation". The correlation established can only be extracted using acoustic based imaging as it requires submillimetric resolution of both the ID and OD profile of each perforation Using this correlation, the resulting set of post-frac perforation exit-hole measurements improves an operators’ ability to complete a holistic well completion evaluation, even when well conditions cause perforations to be plugged. The outcome is improved operational insight through the ability to directly compare stages with plugged perforations to those without. This approach can be applied at any point in the well's life cycle, which allows operators to revisit assessments and virtually unplug obscured and proppant-filled perforations. The methodology requires a sound baseline knowledge of the performance of the downhole perforating charges. The baseline is commonly obtained through a calibration stage, which is a stage of charges that are shot but left unstimulated to provide the control measurements for the specific charge in the given well conditions. Current industry performance of downhole perforating charges is investigating through the aggregated dataset of calibration charges. To validate this solid-state acoustic technology and demonstrates its high degree of accuracy for entry and exit-hole perforation measurements, machined samples were scanned with this technology, and with a metrology-grade laser scanner for comparison. This paper presents a novel virtual unplugging methodology, enabled by highly accurate and validated entry-hole measurements, as well as other insights garnered from the aggregate analysis of the world's largest calibration perforation datasets.
This paper presents a novel approach to a time scale discretization when predicting ESP pump failures at different scales. This study proves that models can be used to formalize failure predictions, prevention, and lead to optimizing the ESP's replacement and/or maintenance. The target parameters reflected two different time scale ranges. In the first approach ‘Time to Failure’ and its corresponding ‘Active Time to Failure’ were predicted. The second case excluded time periods when a well was off-line for other reasons than failure. These two targets (modeling parameters) represented low frequency events and were developed using geological or/and well geometry parameters. The Total Time to Failure model (Production Period model) based on a combined trajectory and geology data set showed acceptable and stable performance. A corresponding model with wellhead parameters summarized across each production period was introduced to complement the large scale analysis. A second group of models of higher resolution was designed to detect failures in real time. In these cases estimations for the probability of a failure at a specific time using the most recent wellhead data while excluding well's non-active time periods related to workovers and other non-productive time periods. These models used pre-processed wellhead data from a few selected wells and pads. Well data required pooling large amounts of data and developing a parameter summarization in time periods based on uninterrupted Motor Current Time Periods. These discrete time periods represented events with or without a failure depending on a reason for the current value to be zero. The probability of a pump failure was estimated using two approaches. In the first approach only the last two ‘periods’ that corresponded to non-failure and failure periods respectively were used. The second approach involved all non-failure periods leading to each corresponding failure period. The first approach overestimated the failures while the second approach overestimated the non-failure events. Initial probability models predicted events with a relatively high success rate. However, more data and additional data transformations are required to verify the practicality of our approach. More refined sub-period estimates in each Current Time Periods may help in developing improved models.
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