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Abstract Drilling long horizontal wells in mature fields in UAE are common and challenging. Some of the associated challenges are; lateral length, geological complexity and rig capabilities. One of the challenges in this case is to provide high RPM and torque to the bit and not to increase the maximum make up torque for the existing drill pipes. Powered rotary steerable systems (RSS), applied within the optimum drilling environment, can improve rate of penetration (ROP), lower risks, and reduce non-productive time (NPT), which can decrease drilling costs. Using through motor telemetry technology, a wired motor with a hollow rotor and flex shaft, allows a connection between rotary steerable systems (RSS) and logging while drilling (LWD) downhole tools. A conductor passes power and communication through the motor to operate and steer the RSS. It delivers higher rev/min and torque directly to the RSS and bit. Using the motorized RSS not only has improved ROP but has also mitigated stick-slip vibration and reduced NPT. The NPT improvements have been identified in areas, such as slip-stick vibration, drill string failures, drill string torque variations and rig equipment failures. To run the Motorized RSS on the UAE oil and gas fields the early planning and risk assessment have been conducted. Experience across the worldwide Halliburton locations, both on and offshore, have been presented to show the benefits of Motorized RSS. With improved performance as a result of increased torque and bit speed, and reduction of the stick-slip vibrations, this motor-driven RSS has delivered superior performance and improved ROP in challenging medium and hard formations. At least two horizontal wells with lateral exposure more than 8400 ft was completed shoe-to-shoe with outstanding average ROP using wired power section. It helps to exceed benchmarks by bringing greater horsepower to the rock destruction process with longer runs and higher ROPs. Implementation of wired power section technology for RSS tools will reduce failure risks either for Haliburton either for Customer rig equipment and allows to extend the lateral section footage to develop targets which was unable to hit before. The technology gives opportunity to use the existing capabilities and reduces costs for purchasing the new drillpipes and maintenance of the rig itself.
Drilling in the Digital Age: Case Studies of Field Testing a Real-Time ROP Optimization System Using Machine Learning
Al-Riyami, N. (Exebenus, Stavanger, Norway) | Revheim, O. (Exebenus, Stavanger, Norway) | Robinson, T. S. (Exebenus, Stavanger, Norway) | Batruny, P. (PETRONAS Carigali, Kuala Lumpur, Malaysia) | Meor Hakeem, M. H. (PETRONAS Carigali, Kuala Lumpur, Malaysia) | Tze Ping, G. (Faazmiar Technology Sdn Bhd, Kuala Lumpur, Malaysia)
Abstract O&G operators seek to reduce CAPEX by reducing unit development costs. In drilling operations this is achieved by reducing flat time and bit-on-bottom time. For the last five years, we have leveraged data generated by drilling operations and machine learning advancements in drilling operations. This work is focused on field test results using a real-time global Rate of Penetration (ROP) optimization solution, reducing lost time from sub-optimal ROPs. These tests were conducted on offshore drilling operations in West Africa and Malaysia, where live recommendations provided by the optimization software were implemented by the rig crews in order to test real-world efficacy for improving ROP. The test wells included near-vertical and highly deviated sections, as well as various formations, including claystones, sandstones, limestones and siltstones. The optimization system consisted of a model for estimating ROP, and an optimizer algorithm for generating drilling parameter values that maximize expected ROP, subject to constraints. The ROP estimation model was a deep neural network, using only surface parameters as inputs, and designed to maximize generalizability to new wells. The model was used out-of-the-box, with no specific retraining for the field testing. During field-tests, increased average ROP was observed after following recommendations provided by the optimizer. Compared to offset wells, higher average ROP values were recorded. Furthermore, drilling was completed ahead of plan in both cases. In the Malaysian test well, following the software's advice yielded an increase in ROP from 10.4 to 31 m/h over a 136 m drilling interval. In the West Africa well, total depth was reached โผ24 days ahead of plan, and โผ2.4 days ahead of the expected technical limit. Importantly, the optimization system provided value in operations where auto-driller technologies were used. This work showcases field-test results and lessons learnt from using machine learning to optimize ROP in drilling operations. The final plug-and-play model improves cycle efficiency by eliminating model training before each well and allows instantaneous, real-time intervention. This deployable model is suitable to be utilized anytime, anywhere, with retraining being optional. As a result, minimizing the invisible lost time from sub-optimal ROP and reducing costs associated with on-bottom drilling for any well complexity and in any location is now part of the standard real-time operation solutions. This deployment of technology shows how further optimization of drilling time and reduction in well cost is achievable through utilization of real time data and machine learning.
- Asia (1.00)
- Africa (1.00)
- North America > United States > Texas (0.29)
- Asia > Malaysia (0.89)
- Africa > West Africa (0.89)
A Unique Engineering Approach in Horizontal Drilling Through Unconsolidated Formations to Minimize Time and Cost Using High-Build-Rate Rotary Steerable Systems in Sultanate of Oman
Omara, Ahmed (Schlumberger) | Alba, Hector (Schlumberger) | Al Yarroby, Faisal (Schlumberger) | Al Abri, Ahmed (Daleel Petroleum) | Al Habsi, Riyad (Daleel Petroleum)
Abstract Drilling horizontal wells with a high dogleg severity (DLS) of 10โ16 deg/30 m is the approach that one operator in Oman adopted to drill the buildup section. The 8ยฝ-in section used to be drilled with a conventional motor BHA, which took around 4 days to complete. Due to the high DLS, it was required to slide at least 80% of the time. This led to a slow drilling rate, hole cleaning issues, and difficulties running the 7-in liner afterward. For a step change to happen, a full directional drilling system had to be reengineered with an extensive study of the BHA and well design. The objective was to reduce the total drilling time in the 8ยฝ-in BUS, improve the borehole quality, and reduce flat time. Traditional rotary steerable systems (RSS) are limited with their steering capabilities. A hybrid, high-build-rate RSS with push- and point-the-bit features offers the capabilities of achieving a DLS of up to 17 deg/30 m as it is independent of outside formation. Implementing the new approach eliminated the long sliding intervals and poor borehole cleaning caused by limited surface rotation with the motor BHA. The system was modeled using finite element drilling dynamics simulation software, with multiple bits and drillstring configurations to optimize the directional results. In addition, compressive study of the mud properties enabled drilling the section safely throughout Nahr Umar shale. Later, the same system was coupled with a high-torque motor, and the results showed an even better performance, which the operator plans to consider in the future to enhance the drilling rate. The use of a hybrid RSS system with a specific bit built for the application has proven its success as an integrated engineered drilling solution. It reduced the 8ยฝ-in section drilling time by 50% with improved borehole quality and delivered an overall ROP that is approximately three times what a motor BHA would have delivered. The improvement is a result of the use of PDC over TCI bits and the elimination of slide drilling. In addition, full rotation and elimination of micro-DLS resulted in smoother liner running operation. While drilling, the 100% rotational steering improved the overall hole cleaning, and the modified mud properties and additives helped eliminate the wiper trips performed previously prior to reaching the reservoir section. The success of this integrated system led the operator to replace all the motors in the entire field. This paper emphasizes the impact of new technology together with effective well engineering in drilling efficiency. With current industry focus on cost control, high-DLS RSS technology introduces new savings when used in the right application. This particular case is very common across the industry and proves the many advantages of integrated engineering projects.
- Overview > Innovation (0.54)
- Research Report > New Finding (0.34)
- Asia > Middle East > Oman > Central Oman > South Oman Salt Basin > Nahr Umr Formation (0.99)
- Asia > Middle East > Oman > Thamama Group > Shu'aiba Formation (0.98)
Application of Machine Learning Techniques for Real Time Rate of Penetration Optimization
Elmgerbi, Asad Mustafa (Montanuniversitรคt leoben) | Ettinger, Clemens Peter (Montanuniversitรคt leoben) | Tekum, Peter Mbah (Montanuniversitรคt leoben) | Thonhauser, Gerhard (Montanuniversitรคt leoben) | Nascimento, Andreas (Federal University of Espirito Santo)
Abstract Over the past decade, several models have been generated to predict Rate of Penetration (ROP) in real-time. In general, these models can be classified into two categories, model-driven (analytical models) and data-driven models (based on machine learning techniques), which is considered as cutting-edge technology in terms of predictive accuracy and minimal human interfering. Nevertheless, most existing machine learning models are mainly used for prediction, not optimization. The ROP ahead of the bit for a certain formation layer can be predicted with such methods, but the limitation of the applications of these techniques is to find an optimum set of operating parameters for the optimization of ROP. In this regard, two data-driven models for ROP prediction have been developed and thereafter have been merged into an optimizer model. The purpose of the optimization process is to seek the ideal combinations of drilling parameters that would lead to an improvement in the ROP in real-time for a given formation. This paper is mainly focused on describing the process of development to create smart data-driven models (built on MATLAB software environment) for real-time rate of penetration prediction and optimization within a sufficient time span and without disturbing the drilling process, as it is typically required by a drill-off test. The used models here can be classified into two groups: two predictive models, Artificial Neural Network (ANN) and Random Forest (RF), in addition to one optimizer, namely genetic algorithm. The process started by developing, optimizing, and validation of the predictive models, which subsequently were linked to the genetic algorithm (GA) for real-time optimization. Automated optimization algorithms were integrated into the process of developing the productive models to improve the model efficiency and to reduce the errors. In order to validate the functionalities of the developed ROP optimization model, two different cases were studied. For the first case, historical drilling data from different wells were used, and the results confirmed that for the three known controllable surface drilling parameters, weight on bit (WOB) has the highest impact on ROP, followed by flow rate (FR) and finally rotation per minute (RPM), which has the least impact. In the second case, a laboratory scaled drilling rig "CDC miniRig" was utilized to validate the developed model, during the validation only the previous named parameters were used. Several meters were drilled through sandstone cubes at different weights on bit, rotations per minute, and flow rates to develop the productive models; then the optimizer was activated to propose the optimal set of the used parameters, which likely maximize the ROP. The proposed parameters were implemented, and the results showed that ROP improved as expected.
- North America > United States (0.68)
- Asia > Middle East > UAE (0.28)
- Asia > Middle East > Saudi Arabia > Eastern Province (0.28)
- Research Report > New Finding (0.34)
- Overview > Innovation (0.34)
Evaluation of Derived Controllable Variables for Predicting Rop Using Artificial Intelligence in Autonomous Downhole Rotary Drilling System
Amadi, Kingsley Williams (Australian College of Kuwait) | Iyalla, Ibiye (Robert Gordon University Aberdeen) | Liu, Yang (University of Exeter, England) | Alsaba, Mortadha (Australian College of Kuwait) | Kuten, Durdica (Australian College of Kuwait)
Abstract Fossil fuel energy dominate the world energy mix and plays a fundamental role in our economy and lifestyle. Drilling of wellbore is the only proven method to extract the hydrocarbon reserves, an operation which is both highly hazardous and capital intensive. To optimize the drilling operations, developing a high fidelity autonomous downhole drilling system that is self-optimizing using real-time drilling parameters and able to precisely predict the optimal rate of penetration is essential. Optimizing the input parameters; surface weight on bit (WOB), and rotary speed (RPM) which in turns improves drilling performance and reduces well delivery cost is not trivial due to the complexity of the non-linear bit-rock interactions and changing formation characteristics. However, application of derived variables shows potential to predict rate of penetration and determine the most influential parameters in a drilling process. In this study the use of derived controllable variables calculated from the drilling inputs parameters were evaluated for potential applicability in predicting penetration rate in autonomous downhole drilling system using the artificial neutral network and compared with predictions of actual input drilling parameters; (WOB, RPM). First, a detailed analysis of actual rock drilling data was performed and applied in understanding the relationship between these derived variables and penetration rate enabling the identification of patterns which predicts the occurrence of phenomena that affects the drilling process. Second, the physical law of conservation of energy using drilling mechanical specific energy (DMSE) defined as energy required to remove a unit volume of rock was applied to measure the efficiency of input energy in the drilling system, in combination with penetration rate per unit revolution and penetration rate per unit weight applied (feed thrust) are used to effective predict optimum penetration rate, enabling an adaptive strategize which optimize drilling rate whilst suppressing stick-slip. The derived controllable variable included mechanical specific energy, depth of cut and feed thrust are calculated from the real- time drilling parameters. Artificial Neutral Networks (ANNs) was used to predict ROP using both input drilling parameters (WOB, RPM) and derived controllable variables (MSE, FET) using same network functionality and model results compared. Results showed that derived controllable variable gave higher prediction accuracy when compared with the model performance assessment criteria commonly used in engineering analysis including the correlation coefficient (R2) and root mean square error (RMSE). The key contribution of this study when compared to the previous researches is that it introduced the concept of derived controllable variables with established relationship with both ROP and stick-slip which has an advantage of optimizing the drilling parameters by predicting optimal penetration rate at reduced stick-slip which is essential in achieving an autonomous drilling system. :
- Europe (0.93)
- North America > United States > Texas (0.28)
- Asia > Middle East > Iran > Khuzestan (0.28)
Improving Rate of Penetration in High Pressure High Temperature Gas Wells by Utilization of Managed Pressure Drilling and Artificial Intelligence
Ghamdi, Ahmed (Saudi Aramco) | Saihati, Ahmed (Saudi Aramco) | Abdelrahman, Mohamed (Saudi Aramco) | Omar, Mahmoud (Saudi Aramco) | Abdulraheem, Abdulazeez (KFUPM)
Abstract Drilling in deep high-pressure high-temperature (HPHT) abrasive sandstone pose significant challenges: low rate of penetration (ROP), bit wear, differential sticking, and wellbore instability issues. These issues are magnified when attempting to drill long laterals in the direction of minimum stress. This paper focuses on the use of Managed Pressure Drilling (MPD) and Artificial Intelligence (AI) analytics to improve ROP. MPD is normally used to help drilling in formations with narrow mud weight window, it achieves this by controlling the surface backpressure to keep the annular pressure in the wellbore above the pore pressure and below the fracture gradient. One key benefit of using MPD is that high mud weight is no longer required, since the Equivalent Circulating Density (ECD) is going to be managed to maintain the overbalance. An example of a well that was drilled using MPD solely for ROP improvement is presented in this paper. This well achieved almost double the ROP of a control well, which was drilled in the same formation with no MPD. Essentially most of the drilling parameters used, which include, pump rate, revolution per minute (RPM), weight on bit (WOB), and other drilling practices, are controlled by the people on the rig. Incorporating AI analytics in the equation, help minimizes human intervention and could achieve further improvement in ROP. After the ROP improvement observed while using MPD, both technologies were combined in a well drilling the same formation. An example is presented for the well drilled using both technologies.
- North America > United States > Texas (0.95)
- Asia > Middle East (0.95)
- North America > United States > Texas > Travis Peak Formation (0.99)
- North America > United States > Mississippi > Travis Peak Formation (0.99)
- North America > United States > Louisiana > Travis Peak Formation (0.99)
- (3 more...)
- Well Drilling > Pressure Management > Managed pressure drilling (1.00)
- Well Drilling > Drilling Operations > Drilling optimization (1.00)
- Well Drilling > Drilling Fluids and Materials > Drilling fluid selection and formulation (chemistry, properties) (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Abstract On a Deep Gas Project in the Middle East, it is required to drill 3500 ft of 8-3/8" deviated section and land the well across highly interbedded and abrasive sandstone formations with compressive strength of 15 - 35 kpsi. While drilling this section, the drill string was constantly stalling and as such could not optimize drilling parameters. Due to the resulting low ROP, it was necessary to optimize the Drill string in order to enhance performance. Performed dynamic BHA modelling which showed current drill string was not optimized for drilling long curved sections. Simulation showed high buckling levels across the 4" drill pipe and not all the weight applied on surface was transmitted to the bit. The drilling torque, flowrate and standpipe pressures were limited by the 4" drill pipe. This impacted the ROP and overall drilling performance. Proposed to replace the 4" drill pipe with 5-1/2" drill pipe. Ran the simulations and the model predicted improved drill string stability, better transmission of weights to the bit and increased ROP. One well was assigned for the implementation. Ran the optimized BHA solution, able to apply the maximum surface weight on bit recommended by the bit manufacturer, while drilling did not observe string stalling or erratic torque. There was also low levels of shocks and vibrations and stick-slip. Doubled the on-bottom ROP while drilling this section with the same bit. Unlike wells drilled with the previous BHA, on this run, observed high BHA stability while drilling, hole was in great shape while POOH to the shoe after drilling the section, there were no tight spots recorded while tripping and this resulted in the elimination of the planned wiper trip. Decision taken to perform open hole logging operation on cable and subsequently run 7-in liner without performing a reaming trip. This BHA has been adopted on the Project and subsequent wells drilled with this single string showed similar performance. This solution has led to average savings of approximately 120 hours per well drilled subsequently on this field. This consist of 80 hours due to improved ROP, 10 hrs due to the elimination of wiper trip and a further 30 hrs from optimized logging operation on cable. In addition, wells are now delivered earlier due to this innovative solution. This paper will show how simple changes in drill string design can lead to huge savings in this current climate where there is a constant push for reduction in well times, well costs and improved well delivery. It will explain the step-by-step process that was followed prior to implementing this innovative solution.
- Asia > Middle East (0.66)
- Africa > Middle East (0.48)
- North America > United States > Texas (0.28)
- North America > Canada > Alberta (0.28)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.54)
- Geology > Geological Subdiscipline > Geomechanics (0.46)
- Well Drilling > Drillstring Design > Torque and drag analysis (1.00)
- Well Drilling > Drillstring Design > Drill pipe selection (1.00)
- Well Drilling > Drilling Operations (1.00)
High Performance Drilling Onshore Abu Dhabi
Kuyken, Chris Wilhelm (AlMansoori Specialized Engineering) | Elkasrawy, Mohamed Elsaied (AlMansoori Specialized Engineering) | Al Breiki, Ali Mubarak Saeed (ADNOC Onshore) | Elgendy, Yahia Abdelfattah Mahmoud (Schlumberger) | Abdelaal, Ahmed Gamal (AlMansoori Specialized Engineering)
Abstract High performance drilling is an approach applied in the drilling of hole sections that are not primarily benefitting from data acquisition except the minimum like gamma ray and directional. Therefore these sections are drilled with high ROP and subsequently cased in support of reducing well costs. High performance drilling leading to continuous ROP optimization has been proven a key enabler for invisible lost time reduction (ILT), being one of the current regional well delivery challenges. In this paper we explain the approach followed by the team comprising of operator, service provider and equipment provider in reducing the impact of ILT during the actual drilling phase. We learnt that creating a performance culture based on rigorously applying of best practices and the eagerness to continuously improve on past performance as a first strategy and the application of novel directional drilling motor technology as the second resulted in ROP performance records. For example in one field an average ROP record was achieved of 188 ft / hour a 15 % improvement from the previous record. We learnt that in particular the communication between all parties i.e. the client office, the service provider and the team on the rig was the most important factor in order to create a shared vision on the need to improve the ROP based on the last ROP performance benchmark. Secondly the latest motor technology and the way of how it gets deployed, available to the team played a major role, and brought the performance level to a new dimension whereby the ROP was targeted to be optimum instead of maximum thereby reducing the risk for NPT related incidents (hole problems, equipment break-down) and repair and maintenance cost becoming cost prohibitive. This paper is specifically meant to share best practices from the last 10 years with the larger UAE drilling community. It is service provider contribution to provide insights for the new generation drilling engineers and directional drillers in safely pushing the drilling performance to higher levels all the time targeting the ILT in hole making. The work has proved that a combination of low torque high speed and high torque low speed can successfully performance drill all vertical hole sizes in the UAE on-shore fields either using tri-cone or PDC bits.Figure 1: High performance motor
Combining Machine Learning and Classic Drilling Theories to Improve Rate of Penetration Prediction
Zhang, Hongbao (Sinopec Research Institute of Petroleum Engineering) | Lu, Baoping (Sinopec Research Institute of Petroleum Engineering) | Liao, Lulu (Sinopec Research Institute of Petroleum Engineering) | Bao, Hongzhi (Sinopec Research Institute of Petroleum Engineering) | Wang, Zhifa (Sinopec Tech Middle East LLC) | Hou, Xutian (Sinopec Research Institute of Petroleum Engineering) | Mulunjkar, Amol (Sinopec Tech Middle East LLC) | Jin, Xin (Sinopec Research Institute of Petroleum Engineering)
Abstract Theoretically, rate of penetration (ROP) model is the basic to drilling parameters design, ROP improvement tools selection and drill time & cost estimation. Currently, ROP modelling is mainly conducted by two approaches: equation-based approach and machine learning approach, and machine learning performs better because of the capacity in high-dimensional and non-linear process modelling. However, in deep or deviated wells, the ROP prediction accuracy of machine learning is always unsatisfied mainly because the energy loss along the wellbore and drill string is non-negligible and it's difficult to consider the effect of wellbore geometry in machine learning models by pure data-driven methods. Therefore, it's necessary to develop robust ROP modelling method for different scenarios. In the paper, the performance of several equation-based methods and machine learning methods are evaluated by data from 82 wells, the technical features and applicable scopes of different methods are analysed. A new machine learning based ROP modelling method suitable for different well path types was proposed. Integrated data processing pipeline was designed to dealing with data noises, data missing, and discrete variables. ROP effecting factors were analysed, including mechanical parameters, hydraulic parameters, bit characteristics, rock properties, wellbore geometry, etc. Several new features were created by classic drilling theories, such as downhole weight on bit (DWOB), hydraulic impact force, formation heterogeneity index, etc. to improve the efficiency of learning from data. A random forest model was trained by cross validation and hyperparameters optimization methods. Field test results shows that the model could predict the ROP in different hole sections (vertical, deviated and horizontal) and different drilling modes (sliding and rotating drilling) and the average accuracy meets the requirement of well planning. A novel data processing and feature engineering workflow was designed according the characteristics of ROP modelling in different well path types. An integrated data-driven ROP modelling and optimization software was developed, including functions of mechanical specific energy analysis, bit wear analysis and predict, 2D & 3D ROP sensitivity analysis, offset wells benchmark, ROP prediction, drilling parameters constraints analysis, cost per meter prediction, etc. and providing quantitative evidences for drilling parameters optimization, drilling tools selection and well time estimation.
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.48)
- Information Technology > Artificial Intelligence > Machine Learning > Decision Tree Learning (0.36)
- Information Technology > Artificial Intelligence > Machine Learning > Performance Analysis (0.34)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.31)
Precise Engineering Design and Technology Integration Delivers the First Successful Shoe to Shoe Run that Enhanced Drilling Efficiency in Highly Intercalated Formations
Krikor, Ara (Schlumberger) | Sanderson, Martin (Schlumberger) | Merino, Lizeth (Schlumberger) | Benny, Praveen (Schlumberger) | Ibrahim, Sameh (Schlumberger) | Al-Khayat, Khaled (Schlumberger) | AlYasiri, Siffien (Schlumberger)
Abstract Drilling highly intercalated formations with Polycrystalline Diamond Compact (PDC) bits has been a challenge in few Southern Iraqi Fields. The established drilling practice for the 17.5-in section has been a two-run strategy - Top section formation is mostly dolomite intercalated with anhydrite drilled with a Tungsten Carbide Insert (TCI) bit, then trip out of hole to change to a PDC bit and drill to section TD. The upper section comprises highly intercalated formations known to induce severe bit and BHA damage. The application of new Conical Diamond Elements (CDEs) backing up traditional PDC cutters on the bit blades had significantly improved bit durability in the bottom half of the section. The subsequent challenge was to apply this CDE technology onto an optimized PDC chassis and achieve a single run section thus eliminating a trip for bit change as well as improving overall Rate of Penetration (ROP) of the section. A Bit and drill string optimization exercise was initiated by the Technology Integration Center to develop a new PDC bit design that could deliver a shoe-to-shoe section. Analysis of offset well data highlighted the need for greater cutter redundancy on the bit to survive high impact loading and optimized cutter arrangement to minimize bit induced instability while drilling through intercalations with highly fluctuating rock strengths. A finite element analysis (FEA)-based modelling system was used to evaluate the dynamic behavior of multiple bit design configurations in various rock scenarios and narrow down to the optimum design for the challenge. The optimization exercise shortlisted a PDC bit design characterized by 8 Blades, 16-mm PDC cutters and CDEs backing-up the nose and shoulder PDC cutting structure. A detailed drilling parameter road map was also generated to ensure optimum drilling parameter application for shoe-to-shoe assurance. The new bit drilled the entire section in single run with a field record average on-bottom ROP of 20 m/hr which was a 11% improvement over the best offset performance with a two-bit strategy. In addition, a trip for bit change was eliminated. A minimum saving of 20 rig hours was realized thus reducing section time by almost one day compared to the offset wells. The bit was pulled out of hole with minor cutter damage indicative of efficient drilling dynamics and opportunities for further performance enhancement through improved parameter management, alternate drive systems and high torque drill pipes. This paper further will discuss how the technology integration and precise engineering design can solve complicated on bottom drilling problems and address the problematic challenges of drilling highly intercalated formations. This strategy enabled a significant time and cost saving compared to drilling the section conventionally.
- Asia > Middle East (0.94)
- Europe > Norway > Norwegian Sea (0.24)
- Well Drilling > Drilling Operations (1.00)
- Well Drilling > Drill Bits > Bit design (1.00)