Day 2 Wed, March 08, 2023最新文献

{"title":"Interoperability of Real-Time Drilling Signals at the Rig Site: An Example Based on Mechanical Specific Energy","authors":"E. Cayeux, B. Daireaux, J. Macpherson, F. Florence, Espen Solbu","doi":"10.2118/212472-ms","DOIUrl":"https://doi.org/10.2118/212472-ms","url":null,"abstract":"Digitalization of the drilling process has the potential to improve drilling data quality and consistency, providing support for drilling optimization, safety and efficiency. A significant barrier to realizing this potential is the data streams from the multitude of service companies, which changes almost daily, with variable definition of each of the real-time signals. This paper provides a solution to this problem: a method describing the semantics of real-time drilling signals in a computer readable format. For illustration, consider the calculation of mechanical specific energy (MSE) in drilling. It is possible to calculate a simple MSE signal in many ways, by using surface or downhole measurements, by applying corrections to the raw data, or by interpreting the equation in alternate ways. There is typically only a delivered value – the underlying details are lost. Semantic graphs bring transparency to the calculation by describing facts about drilling signals that are interpretable by computer systems. This semantic information encompasses details about signal measurement, and about signal calculation, correction, or conversion, yet all without exposing proprietary mathematical methods of calculation. It is possible, using semantic graphs, to assess the meaning and potential application of a signal, and whether or not the quality of the signal is suitable for its intended purpose. A semantic network relies on a vocabulary that defines a specific language dedicated to a particular topic, here drilling signals. The semantic network language is versatile: an existing language can describe new information and newly created signals. This provides a method meeting future needs without having to modify a standard constantly. In practice, each data provider exposes the meaning of its signals in the form of individual semantic networks. Merging these distinct semantic graphs provides a larger set of facts. This opens the possibility for synergies between independent data providers. For instance, applying logical rules infers new information. Since it is possible to query the semantic graph for signals that have certain properties, discovery of the most relevant signals at any time is feasible. By keeping track of modifications made to the semantic network during the drilling operation, it is also possible to post-analyze facts known about the available drilling signals, in an historic perspective. This is essential information for interpreting real-time data during offline data mining. This work is part of the D-WIS initiative (Drilling and Wells Interoperability Standards), a cross-industry workgroup providing solutions to facilitate interoperability of computer systems at the rig site and beyond. The D-WIS workgroup continues to develop the semantic vocabulary. The benefit of a computer interpretable description of the meaning of real-time signal is not limited to signals in real-time. Indeed, the method allows automatic data mining of historical d","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"86 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127444485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pressure On-Demand - Hybrid Electric BOP Control Systems","authors":"M. Givens, M. Olson, Joseph Hope, Samantha Beim","doi":"10.2118/212555-ms","DOIUrl":"https://doi.org/10.2118/212555-ms","url":null,"abstract":"\u0000 Changing well control requirements have increased accumulator volumes for the operation of Blow Out Preventer (BOP) stacks and diverters, challenging the space available for the BOP control system equipment on today's rigs. Koomy or Land Closing Units (LCUs) use potential energy stored in accumulators to control the hydraulically actuated BOP Stack and diverter systems. Accumulator systems are limited in their operational capacity because of the inefficiencies created by rapid adiabatic gas expansion and the pressure and flow decay over the function stroke. By changing the approach from stored energy in accumulators to energy on-demand, the hybrid electric BOP control system will expand the operational capacity drilling rigs.\u0000 The implementation of a hybrid-electric pressure on-demand BOP control system can reliably address this challenge by providing indefinite usable volume and a more reliable platform with a smaller footprint and value-added performance. Feedback loops will provide on demand flow to better match the operation and shear curves of the individual functions, eliminating the BOP closing time and response constraints that challenge traditional accumulator systems. This paradigm shift will remove the usable volume constraint as the pump on-demand system provides an indefinite amount of flow at the maximum rated working pressure of the operating piston.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122068048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Field Deployment of a LSTM Neural Network Tool for the Rock Formation Consolidation Inference of Brazilian Sandstone Reservoirs","authors":"Fabio Rodrigues Gonçalves da Silva, Victor Hugo Ribeiro Carriço, Alexandre Zacarias Ignácio Pereira, André Leibsohn Martins","doi":"10.2118/212486-ms","DOIUrl":"https://doi.org/10.2118/212486-ms","url":null,"abstract":"\u0000 The objective of this work is to present a methodology based on the analysis of drilling parameters to infer if a reservoir formation is well consolidated or not, as a support to the selection of sand control strategies.\u0000 This work proposes a statistical classification model and the usage of a memory based neural network, known as LSTM (long short-term memory) network. This model explores time series characteristics of the problem and it is validated using a cross strategy. Training performance is evaluated using F1-score, which is a metric that balances precision (percentage of true positives compared to false positives) and recall (percentage of true positives compared to false negatives), chosen because the dataset is unbalanced, there are more samples of one class than the other. The dataset consists of pre-tagged wells, each of them with at least nine hours of drilling data.\u0000 Considering 48 cases from different drilled wells, the model was trained to learn how to tag between both patterns. The model analyzes 23 different drilling variables to reach a conclusion.\u0000 After training the model, tests were performed and the results showed a high identification efficiency: around 90% of accuracy. That way, mechanical data analysis from the drilling process plays a very important role, supplementing that information and allowing a better understanding of formation behavior by employing what can be considered full-size and a real-time scratch test. Match the collected data with those from wells in which there is logging information, provides geomechanics calibration, and allows consistent rock profiling. It helps to define not only if there is a need for sand control but also the kind of technique to be applied to the analyzed formation accordingly to its consolidation state. The impact of that information is expressive to the completion process.\u0000 This feature will be very useful in Brazilian post-salt wells that present sandstone as its reservoir rock formation. Also, as this tool was designed to run in a drilling digital twin, it can be automatically run as soon as the total depth is reached in the drilling phase, providing a fast insight to anticipate completion design.\u0000 It is the first time in literature that this approach is used for this specific objective: define if a gravel pack or even any kind of sand control is indeed necessary to be installed based on information gathered while drilling the well. Its great results led this tool to the deployment phase. This work also aims to illustrate the first outcomes of that application in real-time decision-making.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129831797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying a Downhole Drilling Mechanics Tool to Improve Operational Procedures and Rig Operating Systems in Horizontal Wells","authors":"Isaac S Fonseca, M. Isbell, Austin Groover","doi":"10.2118/212520-ms","DOIUrl":"https://doi.org/10.2118/212520-ms","url":null,"abstract":"\u0000 Managing drilling dysfunction is key to safely drilling horizontal wells in harsh environments. However, extended horizontal wells with long open-hole intervals complicate identifying and addressing dynamic drilling problems using surface measurements. The latest generation downhole drilling mechanics tool uses high-frequency measurements to characterize drilling patterns and dysfunctions in turn, is used to improve rig operational practice and drilling system design.\u0000 This paper describes how to capture and analyze downhole drilling data sets, identify areas for improvement, and address them by improving operational rig processes and the drilling system design. The approach is applied with two different operators using two different rig operating systems with two different contractors in two different basins with varying systems of drilling and muds. The drilling process is characterized in different ways, and energy management is closely evaluated using surface and downhole measurements for a comprehensive system perspective.\u0000 The authors break down operational activities into processes and apply process improvement concepts to improve drilling outcomes regarding safety, quality, delivery, and cost. The process improvements are sustained by a combination of drilling system design improvement and drilling operation process automation. Examples of the processes improved throughout a well are the repetitive activities completed during drilling a stand of drill pipe (typically about 95’ in length)), an on-bottom transition with the drilling assembly, rotary drilling, and slide drilling operation with conventional steerable systems, and an off-bottom transition. The improvements have saved a trip for two- and three-mile horizontal sections where multiple runs are typical.\u0000 This paper describes and demonstrates a repeatable approach to using the drilling rig system, downhole drilling system, and body of operational practice to break down the drilling of an interval into processes for improvement. Using a downhole drilling mechanics sub to characterize energy management and qualify operational practice allows a process improvement approach to managing downhole behaviors with drilling rig control systems.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128393405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deployment of a Hybrid Machine Learning and Physics Based Drilling Advisory System at the Rig Site for ROP Optimization","authors":"M. Behounek, K. Mckenna, T. Thetford, T. Peroyea, Michael Roberts, J. Pearce, G. Hickin, P. Ashok, M. Yi, D. Ramos","doi":"10.2118/212515-ms","DOIUrl":"https://doi.org/10.2118/212515-ms","url":null,"abstract":"\u0000 During well construction, automatic monitoring of the sensor signals for drilling dysfunction detection through pattern recognition algorithms is key to improving rate of penetration (ROP) and preventing tool failure. The addition of physics-based models can enable further improvement, but often one is limited by the contextual data needed by these models, as well as the computational power available at the edge. This paper details the successful field deployment of a system that address these challenges.\u0000 The dysfunction tracking algorithms used were built using Bayesian networks as base models and validated using downhole data. Physics based models in the advisory system are used to compute the first five modes of natural frequencies for axial, torsional and lateral vibration. The contextual data required for the calculations consists of the bottom hole assembly (BHA) and survey data. Scripts were deployed to transfer this data directly from the operator's database to rig site.\u0000 This system has been deployed on rigs in the US for over 4 years now, and the fact that they are being actively used to this day is a testament to its success. A key enabler here is the automatic transfer of contextual data from the office database to the rig site. The contextual data used in the model is something the crew have to input into the office database outside the needs of the advisory system. So, a process was already in place to properly record this information, and that worked to the advantage of the system. Eliminating the need to re-enter this data at the rig site was key to the success of this advisory system. Using the physics-based model, critical RPM bands are plotted on the drilling advisory screen to alert the driller whenever they are near an RPM that needs to be avoided. Visual indicators on a weight on bit (WOB)-RPM grid provide guidance to the driller on which direction to move the parameters to avoid dysfunctions and optimize drilling.\u0000 Physics based models nicely complement data-based ML models in an advisory system, but real-world application of such combined systems are limited due to reasons such as timely availability of contextual data at the rig-site, or the need for contextual data that is not readily measured. In this paper, we demonstrate how the problem can be solved, and provide guidance for larger adoption of the process followed by team.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114304379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facts, Fallacies and Pitfalls of Using Mechanical Specific Energy (MSE) – Part 1","authors":"Robello Samuel, G. Mensa-Wilmot","doi":"10.2118/212508-ms","DOIUrl":"https://doi.org/10.2118/212508-ms","url":null,"abstract":"\u0000 Over the years, some researchers have used Mechanical Specific Energy (MSE), which is said to represent the amount of energy needed to drill a unit volume of rock, to quantify drilling efficiency. MSE was originally introduced by Teal for the mining industry in 1964. Since then, MSE has taken different forms for other reasons, based on its interpretation, and intended use. This paper provides a comprehensive review of MSE in general, discusses its different forms and narratives, and draws the readers' attention to common (and not so common) facts, pitfalls, and fallacies, of using MSE.\u0000 It has been found that these specific energy concepts are held as true for all predictive purposes in drilling, amended and promoted beyond the original framework. The paper analyzes all the equations presented in the past and quantifies each component of the equations. The hydraulic terms used alongside the mechanical terms are also discussed. Extensive simulations have been carried out and will be reviewed in this paper by quantifying the energy under each term based on rate of penetration effects and implications. We aimed to demonstrate in this paper, the theoretical grounds for pitfalls and fallacies in using MSE.\u0000 MSE is made up of two components: torsional energy and thrust energy. The results have shown that the thrust term is much smaller than the second torsional energy term and in most of the cases, about 2% or less. Hence, it could be neglected and thereby the equation results in the form of inverse of the rate of penetration (ROP) making the calculated MSE value redundant when the actual ROP is available. The results also have shown that when the hydraulic energy term is subtracted from the MSE equation, it results in negative rate of penetration and thereby shows a fundamental flaw in the system formulation. The purposed and merits of MSE use, by some researchers to identify drilling dysfunctions, will also be highlighted. In this process, it has been shown that nonlinear \"torque wedging\" causes inaccuracies in dysfunctions identification and discussions. Also, the field data presented in the paper shows that mechanical energy is not a ratio of input energy and rate of penetration. Moreover, none of the studies have accounted accurately for the effects of bit wear and motor wear on MSE. It has been found that overall, the concept relating to dysfunction quantification is a self-destructive process, which has spread from paper to paper without the required checks and verifications for accuracy. The underpinning discussions have been backed and demonstrated with numerical examples.\u0000 The paper provides the pitfalls in the omissions of some of the assumptions in various MSE models used by engineers. This helps the users to carefully plan, design, engineer and construct the wells.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121673805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-minimizing kinematics for actively morphing flapping-foil thrusters","authors":"D. Anevlavi, E. Filippas, K. Belibassakis","doi":"10.5957/some-2023-017","DOIUrl":"https://doi.org/10.5957/some-2023-017","url":null,"abstract":"Bio-inspired thruster designs based on flapping-foils have the potential to achieve high efficiency and stealth, thus allowing for an extension of the overall operational capabilities of autonomous underwater vehicles (AUVs) propelled solely using foils. In this work, we produce thruster designs with enhanced propulsive performance by introducing prescribed chordwise and spanwise changes in the geometry during each flapping-cycle, i.e. active morphing, with optimally tuned parameters to further mimic aquatic locomotion. The reference design performs a thrust-producing combination of out-of-phase heaving and pitching motions, whereas for the evaluation of each candidate design, a cost-effective GPU-accelerated boundary element solver (BEM) is proposed.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"120 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132352812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Successful Field Implementation of an Integrity-Focused Digital Monitoring System for Tubular Running in a Challenging Thermal ERD Application","authors":"S. Taubner, Marius Bordieanu, Daniel Dall'Acqua","doi":"10.2118/212482-ms","DOIUrl":"https://doi.org/10.2118/212482-ms","url":null,"abstract":"\u0000 Horizontal liners in extended-reach drilling (ERD) wells can experience severe loading during running. Sometimes, downhole loads approach the limits of the tubular system and must be actively managed to ensure long-term well integrity. This paper describes a Canadian thermal operator's approach to managing installation and service performance of slotted liner and wire-wrapped screen systems in a steam-assisted gravity drainage (SAGD) application with unwrapped reach ratios approaching 13:1, and the associated evolution of liner running practices.\u0000 The Operator's approach combines well-characterized liner body installation loading limits and a rigsite digital solution that leverages available measurements and a real-time torque-and-drag and tubular integrity monitoring system to inform the drilling team during running. Surface loads and rates measured by the rig are used as input to top-down torque-and-drag analysis to estimate downhole load distributions. Those downhole load estimates are then compared to the local loading limits of the liner at all depths. These local loading states (and their associated uncertainties) are integrated into a safe surface loading envelope that is displayed to the drilling team and updated in real time to support running decisions.\u0000 The evolution of the Operator's running practices has provided a strong basis for confidence in protecting a critical tubular system, and over 250 liner runs have been monitored to date using the digital system. Prior to implementing the system, a conservative approach to managing downhole loads during liner running was used. The integration of a strong engineering basis for the tubular structure with top-down torque-and-drag analysis and uncertainty characterization has provided a running optimization basis and measurable indicators of tubular health that can serve as an enduring quality record and be referenced for the remainder of the well life. Forecasting of running loads and liner limits to total depth has also enabled early recognition of running challenges and opportunities for optimization.\u0000 Interestingly, the edge-deployed digital system has also led to operational efficiencies during the running process. Running stages involving higher risk to tubular integrity are recognized early and treated with due care, as are opportunities for increasing the efficiency of certain parts of the running process. As the Operator considers longer-reach wells, the system also provides insights into likely running challenges and provides strong history-match datasets that provide a field-calibrated basis for predicting running and tubular integrity limits.\u0000 The Operator leveraged a novel digital methodology for monitoring liner system integrity during well construction. The ongoing use of this system has allowed optimization of planning, real-time, and post-run practices, and provides a well-conditioned historical dataset for future well planning. The methodology has enabled the Operator to ","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130056062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From preventive to predictive maintenance of ship hulls: The role of SHM","authors":"Nicholas E. Silionis, K. Anyfantis","doi":"10.5957/some-2023-033","DOIUrl":"https://doi.org/10.5957/some-2023-033","url":null,"abstract":"Current maintenance procedures for ship hulls are based around a series of time-fixed on-site surveys. The vision for the future of the maritime industry revolves around condition-based hull structural maintenance. The methods and techniques associated with realizing this vision fall within the field of Structural Health Monitoring. The goal of this article is to present the opportunities offered by the design and implementation of hull SHM systems which will enable the transition towards predictive maintenance. The primary focus will be to discuss the different aspects of such a framework as well as potential challenges associated with its development and implementation.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114180151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Failure Knowledge Graphs","authors":"Bojan Vucinic","doi":"10.5957/some-2023-011","DOIUrl":"https://doi.org/10.5957/some-2023-011","url":null,"abstract":"Failure analysis is the cornerstone of asset management via life-cycle costs optimizations. Knowledge graphs are semantic nets that are the next level of database technology. Machine learning (ML) is a field of inquiry devoted to understanding and building methods that “learn”, that is, methods that leverage data to improve performance on some set of tasks. We propose to structure the machine learning data into knowledge graphs to foster advanced failure analysis leveraging optimum life-cycle costs where costs are considered in the largest possible sense including the cost of human life preservation (safety) and the cost (impact) on the environment.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120938494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}