Thorough preplanning and best drilling practices are effective in reducing stuck pipe incidents, data analytics offer additional insight into further reducing the significant non-productive time (NTP) that results from this unplanned event. The severity of the stuck pipe problem may stop the drilling operations for a short time, or in more difficult cases, the drill string has to be cut and the borehole is sidetracked or plugged and abandoned. Consequently, detecting the early signs of this problem, in order to take the right actions, may considerably or entirely reduce the risk of a stuck pipe. Although computational models have been proposed for the early detection of the stuck pipe incidents, the models are derived from a reduced set of wells with stuck pipe incidents, which may result in under-trained models that predict a large number of false positive alarms. A sufficient amount of data or wells that statistically represent the parameters surrounding stuck pipe incidents under different circumstances is required in order to derive a generalizable and accurate prediction model. For this, we first derived a framework to automatically and systematically extract relevant data from the historical data. As such, our framework searches through the historical data and localizes the surface drilling and rheology parameters surrounding the stuck pipe incidents. Moreover, we performed feature selection by selecting the top-ranked parameters from the analysis of variance, which measures the capability of the drilling and rheology parameters to discriminate between stuck pipe incidents and normal drilling conditions, such as, weight on bit, revolutions per minute, among others. Using the relevant features selected by the analysis of variance, we derived a robust and fast classification model based on random forests that is able to accurately detect stuck pipe incidents. The implemented framework, which includes the automated data extraction module, the analysis of variance for feature selection, and prediction, is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew in order to minimize or avoid the NTP due to a stuck pipe.
The earlier a stuck pipe incident is predicted and mitigated, the higher the chance of success in freeing the pipe or avoiding severe sticking in the first place. Time is crucial in such cases as an improper reaction to a stuck pipe incident can easily make it worse. In this work, practical machine learning, classification models were developed using real-time drilling data to automatically detect stuck pipe incidents during drilling operations and communicate the observations and alerts, sufficiently ahead of time, to the rig crew for avoidance or remediation actions to be taken. The models use machine learning algorithms that feed on identified key drilling parameters to detect stuck pipe anomalies. The parameters used in building the system were selected based on published literature and historical data and reports of stuck pipe incidents and were analyzed and ranked to identify the ones of key influence on the accuracy of stuck pipe detection via a nonlinear relationship. The model exceptionally uses the robustness of data-based analysis along with the physics-based analysis. The model has shown effective detection of the signs observed by experts ahead of time and has helped with providing enhanced stuck pipe detection and risk assessment. Validating and testing the model on several cases showed promising results as anomalies on simple and complex parameters were detected before or near the actual time stuck pipe incidents were reported from the rig crew. This facilitated better understanding of the underlying physics principles and provided awareness of stuck pipe occurrence. The model improved monitoring and interpreting the drilling data streams. Beside such pipe signs, the model helped with detecting signs of other impeding problems in the downhole conditions of the wellbore, the drilling equipment, and the sensors. The model is designed to be implemented in the real-time drilling data portal to provide an alarm system for all oil and gas rigs based on the observed abnormalities. The alarm is to be populated on the real-time environment and communicated to the rig crew in a timely manner to ensure optimal results, giving them sufficient time ahead to prevent or remediate a potential stuck pipe incident.
The earlier a stuck pipe incident is predicted and mitigated, the higher the chance of success in freeing the pipe or avoiding severe sticking in the first place. Time is crucial in such cases as an improper reaction to a stuck pipe incident can easily make it worse. In this work, a novel and practical model was developed using real-time drilling data to automatically detect leading signs of stuck pipe during drilling operations and communicate the observations and alerts, sufficiently ahead of time, to the rig crew for avoidance or remediation actions to be taken. The model uses key drilling parameters to detect abnormal trends that are identified as leading signs to stuck pipe. The parameters and patterns used in building the system were identified from published literature and historical data and reports of stuck pipe incidents. The model is designed to be implemented in the real-time drilling data portal to provide an alarm system for all oil and gas rigs based on the observed abnormalities. The alarm is to be populated on the real-time environment and communicated to the rig crew in a timely manner to ensure optimal results, giving them more time to prevent or remediate a potential stuck pipe incident. Testing the model on several wells showed promising results as anomalities were detected early in time before the actual stuck pipe incidents were reported. It further facilitated better understanding of the underlying physics principles and provided awareness of stuck pipe occurance. It improved monitoring and interpretating the drilling data streams. Beside such pipe signs, the model helped detecting signs of other impeding problems in the downhole conditions of the wellbore, the drilling equipment, and the sensors. The model exceptionally uses the robustness of data-based along with the physics-based analysis of stuck pipe. This hybrid model has shown effective detection of the signs observed by experts ahead of time and has helped providing enhanced stuck pipe prediction and risk assessment.
Energized fluids are defined as fluids with one or more compressible gas components, such as CO2, N2, or any combination of gases, dispersed in a small volume of liquid. Generally, these fluids offer an attractive alternative to conventional stimulation fluids in many cases such as low reservoir pressure, water-sensitive formations, and/or the need for shorten flowback period. Energized fluids have many challenges such as low stability at high temperature, high friction pressure during pumping, corrosion in the case of using CO2, and the need for specialized surface pumping equipment. The objective of this paper is to describe the typical components of energized fluids and their effect on the fluid performance. Also, lab testing methods used to evaluate energized fluids performance will be discussed in detail. Foam is a class of energized fluid used for different applications including acidizing, hydraulic fracturing, and fluid diversion. For each application, foam should have a minimum acceptable value of viscosity, stability, and/or fluid compatibility. Those values were reviewed from literature and categorized based on reservoir conditions. Also, different rheological models are analyzed to understand foam flow behavior in both tubing and porous media. Finally, the mechanism of foam transport in porous media is reviewed in this report, which gives insight into foam stability and propagation. The most common application of nitrogen is in artificial lifting, while supercritical CO2 is proposed for condensate banking removal. Selection of the right surfactant, like alpha olefin sulfonates, which are thermally more stable than alkyl ether sulfates, is crucial while designing foam treatment, as they produce the most persistent foams at high salinity and elevated temperatures in the presence of synthetic and crude oils. Currently available foam-based fracturing fluid systems in the industry have temperature limitations to 300°F. The crosslinked gelled foam has a better temperature range than the viscoelastic foam fluid system, whereas non-crosslinked biopolymer-based foam fluid showed better proppant pack cleanup characteristics. In a recent report, the addition of 0.1% silica nanoparticles along with cationic surfactant was shown to enhance CO2 foam stability by 13 hours. In this review, all these aspects of energized fluids are well reported from literature. In this paper, we discuss findings from different lab testing and field demonstration of energized fluids. Compositional modelling for hydraulic fracturing with energized fluids is also reviewed to add insight on fracture geometry estimation. This paper provides guidelines and recommendations for selecting the right energized fluids for successful stimulation treatment.
Primary cementing operations rank among the more important events that occur during a well's lifetime. The cement sheath plays a critical role in establishing and maintaining zonal isolation in the well, supporting the casing and preventing external casing corrosion. For many years, the industry has employed strategies to promote optimal cement placement results. These strategies, collectively known in the industry as good cementing practices. Job execution is the key to insure success of the job based on the designed. New technology that give us optimum execution evaluation (OEE) has been developed to enhance cement job execution by overlapping the design parameter over with the execution parameter real time. The OEE technology significantly improves cementing operations, enabling operators to monitor, control, and evaluate cement placement in real time. OEE combines job design data with acquisition data from both the rig and the cementing equipment to provide a more accurate representation of the job as it is being run. In this paper, we present the process that we completed with detailed operational setup to allow us to monitor and record all parameters related to the cement job execution and the work flow implemented to be able to evaluate the cement job design and execution to achieve the required objectives. This study is also setting the basis to establish development of real time automated cementing advisory system.
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