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Today, drill bits and mud motor issues can account for more than half of the reasons for pulling out of hole before total depth (TD) on directional drilling wells. The complete paper presents a methodology designed for optimally matching drill bits, mud motors, and bottomhole-assembly (BHA) components for reduced failure risks and improved drilling performance. Work Flow The overall work flow includes detailed modeling of each sophisticated component and an algorithm to combine them efficiently at the system level without losing their specific nature. The drill-bit model is created in 4D—3D space modeling plus the transient behavior with time. The detailed cutting structure model may include specifying the number of cutters and how to place them in a 3D cutter space.
Achieving and sustaining performance drilling’s intended benefits—improved drilling efficiency with minimal downhole tool failures and the associated reductions in project cycle time and operational costs—requires new protocols in drilling-system analysis. Drilling-system components [bits, reamers, bottomhole assemblies (BHAs), drive systems, drilling parameters, and hydraulics] must be analyzed independently for their relevance on the basis of application types and project challenges. Additionally, the drilling system must undergo holistic evaluations to establish functional compatibility and drilling-parameter responses and effects, considering project objectives and key performance indicators. This comprehensive physics-based approach ensures durability and rate-of-penetration (ROP) improvements without compromising stability and downhole tool reliability. The success of this process is strongly dependent on vibration control.
Implementing a physics-based digital twin of a drilling system can enable the drilling team to leverage data at each stage of the engineering process to deliver more-consistent, repeatable drilling performance and improved borehole quality, which in turn enables drilling farther and faster while increasing downhole tool life. The complete paper discusses a new performance-evaluation methodology that combines bottomhole assembly (BHA) modeling with field data. BHA modeling simulates the drilling process accurately to establish key performance indicators (KPIs) to help optimize BHA designs to deliver improvements in drilling performance and wellbore quality. The model also can estimate quantities such as microtortuosity that are not directly measured by standard equipment. Importance of Effective BHA Performance Evaluation Determining the cumulative effect of BHA behavior during drilling on the quality of the wellbore and the subsequent impact on performance and life of the BHA is an important goal for improving overall drilling and well-delivery efficiencies.
Deep Earth Energy Production, or DEEP, says a positive well test from its first geothermal project represents a historic milestone. The Canadian company reports that its Border-5HZ is the deepest horizontal well ever drilled in Saskatchewan and is also the world’s first 90° horizontal fluid production well to be drilled and hydraulically fractured for a geothermal power-generation application. The initial results of the 20-stage stimulation and subsequent modeling “indicate a highly productive well—twice the productivity of an unstimulated well,” the company said in its announcement. DEEP expects that the well will achieve commercial production rates of around 100 liters per second (~26 gal). The field plan is to use six producing wells and four injectors to generate up to 20 MW of power.
To state the obvious, it’s been a turbulent 2020 for the oil and gas industry. We’ve come up against continued weakness in commodity pricing, reductions in capex and opex by virtually all operating companies, and frequent demonization by the popular media. In addition, the outbreak of the coronavirus effectively brought the global economy to its knees. Though it seems grim, I can say with confidence that the industry will overcome these challenges, as it always does. What we do—our critical mission of providing the world with abundant, low-cost energy—is here to stay.
Yang, Chen (School of Naval Architecture, Dalian University of Technology ) | Li, Hongxia (School of Naval Architecture, Dalian University of Technology ) | Zhou, Xiaoyu (School of Naval Architecture, Dalian University of Technology ) | Huang, Yi (School of Naval Architecture, Dalian University of Technology )
The capsizing of a ship is a complex phenomenon with strong nonlinearity and rarity. This work describes the numerical implementation of estimating capsizing probability by the Split-Time method. The common roll motion of the ship is simulated by a 1-DOF model and the roll motion in pure loss of stability is by a new 1.5-DOF model. According to time course of movement, the capsizing probability is calculated by Split-Time method and verified by MonteCarlo method. The advantage and reliability of the Split-Time method is proved.
The large annual number of ship losses and the associated economic and environmental costs occur in world's oceans, which necessitate the development of researching ship capsizing. Because of the complex ship dynamics resulting from fluid–structure interaction in random seas, the current criteria for the evaluation of intact stability of ships developed by the International Maritime Organization (IMO) are based on ship restoring capabilities in still water (IMO2009).
Dynamic stability failures of ship, which is difficult to analyze, may occur through a variety of scenarios. Therefore the necessity of improved intact stability criteria is noted and researchers worldwide are working on the improvement of knowledge on the intact stability.
Spyrou (2011) studied some kinds of dynamic instability in following and quartering seas. Bulian (2005) described the variety of stability with general procedure for the analytical approximation of the GZ curve and its use in time domain simulations. Belenky (2010) simulated the ship motion in irregular waves and computed the instantaneous GZ curve at each time step of the simulations, which make it clear that pure loss of stability is a complex dynamical phenomenon.
The capsizing of ship in the actual sea conditions is necessarily researched by probabilistic method in statistics because it is a random events of complex systems. There is not a reliable method for capsizing probability determination because of the rarity and nonlinearity.
Pragma is bringing the industry’s first 3D metal printed, ultrahigh expansion bridge plug to market, the Aberdeen-based company said in a press release. Its patented M-Bubble bridge plug has successfully completed final lab testing and is due to begin field trials by the end of 2020. Initially targeted at both the plug-and-abandonment (P&A) sector and water shutoff applications, the first M-Bubble addresses a gap in the market for a lower-cost, fast-turnaround, permanent plugging solution, with a high pressure differential (3,000 psi) capability, the company said. The plug can be set without additional cement to save rig time and “waiting-on-cement” time, which can accumulate significant savings for the operator, especially in deeper, extended-reach wells. It also provides barrier-integrity reassurance when there is the possibility of a poor cement bond or cement channeling occurring on the high side of deviated wells, the company added.
The fiber-optic distributed temperatrue sensor (DTS) has been used for flow profiling in horizontal multi-stage fractured wells, and there were some reservoir/wellbore coupled thermal models presented by researchers. Although current theoretical models are developed for some certain application scenarios, the industry have realized the great potential of DTS for production prediction in unconventional resources. This paper presents a DTS flow profiling case for a horizontal multi-stage fractured well in tight gas reservoirs with open-hole packer completion scenarios by applying a newly improved theoretical model.
In this paper, we started with the conventional semi-analytical wellbore-fracture-reservoir coupled flow/thermal model which have been developed for cased, perforated, and multi-stage fractured wells, and revised it to consider the special feature of openhole packer completion scenario. Since the formation fluid firstly flows through the fracture into the open-hole annular space between formation and the packer liner, then flow along the annular space until meet the frac port on the production pipe, we add a simulation sub-region representing open-hole annular which helps to understand the flow and heat transfer inside it. The presented model successfully simulated the two-fold flow regime caused by the simultaneous flow and heat transmission in the annular space and the production pipe. In each stage, the DTS temperature data possibly show double drops due to Joule-Thompson cooling effects at the fracture and frac port locations if they are not consistent.
With the improved mathematical model, DTS monitoring data during a three-rate production test in a horizontal multi-stage fractured well in Erdos Basin of China was simulated and analyzed. The improved model with open-hole packer completion was applied and then the gas rate prediction was accomplished.
Ashena, Rahman (Bear & Brook Consulting, Australia and AsiaPacific University, Malaysia) | Rabiei, Minou (University of North Dakota, USA) | Rasouli, Vamegh (University of North Dakota, USA) | Mohammadi, Amir H. (University of KwaZulu-Natal, South Africa)
Proper selection of the drilling parameters and dynamic behavior is a critical factor in improving drilling performance and efficiency. Real-time monitoring allows the driller to avoid detrimental drill string vibrations and maintain optimum drilling conditions through periodic adjustments of various dynamic control parameters (such as weight on bit, rotary speed, circulation rate). However, selection of the appropriate parameters is not a trivial task. A few iterations in parameter modification may be essential before the desired target rate of penetration (ROP) is obtained; however, the final result may not be optimal yet. Therefore, the development of an efficient artificial intelligence (AI) method to predict the appropriate control parameters is critical for drilling optimization.
The AI approach presented in this paper uses the power of optimized Artificial Neural Networks (ANN) to model the behavior of the non-linear, multi- input/output drilling system. The optimization of the model was achieved by optimizing the controllers (combined Genetic Algorithm, GA and Pattern Search, PS) to reach the global optima, which also provides the drilling planning team with a quantified recommendation on the appropriate optimal drilling parameters. Development of the optimized ANN model used drilling parameters data which were recorded real-time from drilling practices in different lithological units. Representative portions of the data sets were utilized in training, testing and validation of the model.
The results of the analysis has demonstrated the AI method to be a promising approach for simulation and prediction of the behavior of the complex multi-parameter drilling system. This method is a powerful alternative to traditional analytic or real-time manipulation of the drilling parameters for mitigation of drill string vibrations and invisible lost time. The utilization can be extended to the field of drilling control and optimization, which can lead to a great contribution of 73% in reduction of the drilling time.
This work demonstrates the capability of the optimizing controller (combination of GA and PS) to improve the efficiency and accuracy of the conventional ANN for drilling optimization.
Tello, Roberto Horacio (Repsol Oil & Gas Malaysia) | Wong, George (Repsol Oil & Gas Malaysia) | Ghosh, Amitava (Baker Hughes) | Chatterjee, Avirup (Baker Hughes) | Santhamoorthy, Priveen Raj (Baker Hughes)
This paper presents a case study on usefulness of having geomechanical understanding to avoid drilling risks and minimize costs in a Drilling campaign, Offshore Peninsular Malaysia.
Significant drilling issues related to wellbore stability were encountered in the four exploration wells drilled during previous campaigns. The development wells were planned to drill with high inclinations (up to 700), through weak coal layers and cross major fault. The drilling problems of the offset wells were critically analyzed to understand the mechanisms of failures. A geomechanical model was built using the offset well information. The well-calibrated stress model was then used for the wellbore stability modeling for the planned trajectories. The outcome of this study was used as key input for casing and mud design.
With the help of the reccomendations based on this study, six highly inclined development wells were drilled through low pressure and narrow mud window intervals without operational problems. Reducing non-productive time (NPT) for the entire drilling campaign was one of the key focus for delivering the wells safely within schedule and budget. This paper documents the entire workflow and methodologies used for this entire study.