Successful Collision Risk Management with Realtime Advanced Survey Correction in A Challenging Horizontal Well

2021 ◽  
Author(s):  
Tongkum Tossapol ◽  
Khamawat Siritheerasas ◽  
Feras Abu Jafar ◽  
Trinh Dinh Phu ◽  
Pham Nam Hieu
Keyword(s):  
Risk Management ◽  
Real Time ◽  
Separation Factor ◽  
3D Visualization ◽  
Management Technique ◽  
Collision Risk ◽  
Time Saving ◽  
Complex Design ◽  
Measurement While Drilling ◽  
Difficulty Index

Abstract The Well X in Nong Yao field, is an infill-well designed for the Gulf of Thailand which presented several interesting challenges due to its complexity, tortuosity, and potential collision risks with other wells. This paper demonstrates the application of a Real-time Advanced Survey Correction (RASC) with Multi Station Analysis (MSA) to correct the Measurement While Drilling (MWD)'s azimuth. The Well X is a 3D Complex design with a high drilling difficulty index (DDI) at 6.9, high tortuosity of 316 degree, and which has an aggressive build on inclination and azimuthal U-turning well path. The well also creates difficult doglegs severity (DLS) up to 5.5deg/100ft, which is near the limit of the flexibility required to achieve the horizontal landing point. The conventional MWD survey, with proximity scanning with the nearby Well A, demonstrates high risk with a calculated Oriented Separation Factor (OSF) of 1.01. The RASC-MSA method is applied with a clearly defined workflow during execution in real-time and provide significant improvement in calculated OSF. RASC-MSA is applied for every 1,000 ft interval drilling below the 9.625in casing shoe. The workflow ensures that the directional driller follows the corrected survey along the well path and especially in the last 300 ft before reaching the electrical submersible pump (ESP) tangent section. The result from RASC-MSA, indicated a 29 ft lateral shift on the left side of the MWD standard surveys. Without this technique, Well X has a high potential to collide with Well A and Well B (Figure 1) as the actual OSF may less than 1 while drilling. The final 3D Least Distance proximity scanning with Well A shows a minimum OSF = 1.35, which is a 30% improvement compared to the conventional MWD survey. Another nearby well, Well B, indicates a minimum OSF=1.66 and passed the anti-collision OSF rule. In consideration of the drilling efficiency, availability, cost effectiveness and time saving, the RASC-MSA analysis to correct the MWD's azimuth are applied and the separation factor can be improved by 30%. In conclusion, the collision risk management technique applied successfully met the complex challenges of Well X, which was successfully drilled and safely delivered. Figure 1 3D visualization to exhibit the collision issue between Well X and nearby existing Wells A and Well B.

scholarly journals Probe-Based Real-Time qPCR Assays for a Reliable Differentiation of Capripox Virus Species

2021 ◽  
Vol 9 (4) ◽  
pp. 765
Author(s):  
Janika Wolff ◽  
Martin Beer ◽  
Bernd Hoffmann
Keyword(s):  
Real Time ◽  
Animal Health ◽  
Diagnostic Methods ◽  
Virus Species ◽  
Time Saving ◽  
Robust Detection ◽  
Highly Sensitive ◽  
Reliable Differentiation ◽  
World Organization ◽  
Real Time Qpcr

Outbreaks of the three capripox virus species, namely lumpy skin disease virus, sheeppox virus, and goatpox virus, severely affect animal health and both national and international economies. Therefore, the World Organization for Animal Health (OIE) classified them as notifiable diseases. Until now, discrimination of capripox virus species was possible by using different conventional PCR protocols. However, more sophisticated probe-based real-time qPCR systems addressing this issue are, to our knowledge, still missing. In the present study, we developed several duplex qPCR assays consisting of different types of fluorescence-labelled probes that are highly sensitive and show a high analytical specificity. Finally, our assays were combined with already published diagnostic methods to a diagnostic workflow that enables time-saving, reliable, and robust detection, differentiation, and characterization of capripox virus isolates.

Fiscal stabilisation in real time: an exercise in risk management

2021 ◽  
Author(s):  
Martin Larch ◽  
Diederik Kumps ◽  
Alessandro Cugnasca
Keyword(s):  
Risk Management ◽  
Real Time

Human versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design

2021 ◽  
pp. 1-38
Author(s):  
Joshua Gyory ◽  
Nicolas F Soria Zurita ◽  
Jay Martin ◽  
Corey Balon ◽  
Christopher McComb ◽  
...  
Keyword(s):  
Real Time ◽  
Design Process ◽  
Process Management ◽  
Superior Performance ◽  
Team Members ◽  
Engineering Teams ◽  
Complex Design ◽  
Data Driven Approach ◽  
Complex Engineering ◽  
Process Managers

Abstract Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an Artificial Intelligent (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams' actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers' intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from the both the AI and human manager as equally relevant and helpful, and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process.

Combined Application of Deep Boundary Detection Tool, Multilayer Data Inversion and 3D Visualization of Seismic Data in Real Time for Geosteering on the Oilfield in the Russian Federation

2021 ◽  
Author(s):  
Chingis Oshakbayev ◽  
Roman Romanov ◽  
Valentin Vlassenko ◽  
Simon Austin ◽  
Sergey Kovalev ◽  
...  
Keyword(s):  
Real Time ◽  
Oil Recovery ◽  
Seismic Data ◽  
3D Visualization ◽  
Structural Data ◽  
Boundary Detection ◽  
Integrated Analysis ◽  
Horizontal Section ◽  
Recovery Method ◽  
Detection Tool

Abstract Currently drilling of horizontal wells is a common enhanced oil recovery method. Geosteering services are often used for accurate well placement, which makes it possible to achieve a significant increase in production at relatively low cost. This paper describes the result of using seismic data in three-dimensional visualization for high-quality geosteering using a deep boundary detection tool and multilayer inversion in real time. Crossing the top of the reservoir while drilling horizontal sections at the current oilfield is unacceptable, due to the presence of reactive mudstones. In case of crossing the top of reservoir, further work on running and installing the liner becomes impossible due to instability and may lead to well collapse. Based on prewell analysis of the structural data, the well was not supposed to approach the top of the target formation along the planned profile. However, while preparing geosteering model and analyzing seismic data it became possible to reveal that risk, elaborate its mitigation and eventually increase the length of the horizontal section. Such integrated analysis made it possible to maintain the wellbore within the target reservoirs, as well as to update the structural bedding of the top based on the multilayer inversion results.

scholarly journals The place of artificial intelligence in the risk management process

2021 ◽  
Vol 120 ◽  
pp. 02013
Author(s):  
Petya Biolcheva
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Risk Management ◽  
Real Time ◽  
Risk Identification ◽  
Management Process ◽  
Whole Process ◽  
Risk Management Process ◽  
Problematic Issue ◽  
The Individual

In recent years, there has been increasing talk of the rapid entry of artificial intelligence into risk management. All the benefits it would bring over the whole process are often commented on: real-time results, processing large amounts of data, more complete risk identification, more accurate risk assessment, etc. There are also negative moods that make various experts feel threatened by their need to be replaced by artificial intelligence. Another problematic issue that arises is related to the transparency of algorithms and the increase in cyber risks [6]. This material aims to identify the individual elements at the stages of risk management in which artificial intelligence (AI) can and should be applied alone, in combination with expert opinion or not. Here it is shown that because of the use of AI the efficiency of the whole process is significantly increased, first of all by conducting in-depth analyses, and the decisions are made by the risk management experts. This proves its usefulness and increases the confidence of experts in it.

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