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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.
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.
Al Gharbi, Salem (King Fahd University of Petroleum & Minerals) | Al-Majed, Abdulaziz (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals) | Patil, Shirish (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals)
Drilling is considered one of the most challenging and costly operations in the oil and gas industry. Several initiatives were applied to reduce the cost and increase the effectiveness of drilling operations. One of the frequent difficulties that faces these operations is unexpected drilling troubles that take place and stops the operation, resulting in losing a lot of time and money, and could lead to safety issues culminating in a fatality situation. For that, the industry is in continues efforts to prevent drilling troubles. Part of these efforts is utilizing the artificial intelligence (AI) technologies to identify troubles in advance and prevent them before maturing to a serious situation. Multiple approaches were tried; however, errors and significant deviation were observed when comparing the prediction results to the actual drilling data. This could be due to the improper design of the artificial intelligent technology or inappropriate data processing. Therefore, searching for dynamic and adequate artificial intelligent technology and encapsulated data processing model is very essential.
This paper presents an effective data-mining methodology to determine the most efficient artificial intelligent technology and the applicable data processing techniques, to identify the early symptoms of drilling troubles in real-time. This methodology is CRISP-DM that stands for Cross Industry Standard Process for Data Mining. This methodology consists of the following phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. During these phases, multiple data-quality techniques were applied to improve the reliability of the real-time data.
The developed model presented a significant improvement in identifying the drilling troubles in advance, compared to the current practice. Parameters such as hook-load and bit-depth, were studied. Actual data from several oil fields were used to develop and validate this smart model. This model provided the drilling engineers and operation crew with bigger window to mitigate the situation and resolve it, prevent the occurrence of several drilling troubles, result in big time and cost savings. In addition to the time and cost savings, CRISP-DM provided the artificial intelligent experts and the drilling domain experts with a framework to exchange knowledge and sharply increase the synergy between the two domains, which lead to a common and clear understanding, and long-term successful drilling and AI teams collaboration.
The novelty of this paper is the introduction of data-mining CRIPS methodology for the first time in the prediction of drilling troubles. It enabled the development of a successful artificial intelligence model that outperformed other drilling troubles prediction practices.
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.