Drilling in the Appalachian basin in Pennsylvania has evolved since its inception. Operators have shifted their focus from mere wellbore delivery to delivering wells in the shortest amount of time to reduce risks and costs, as well as drive efficiency. This paper presents a case study in which offline cementing helped improve operation efficiency by reducing drilling times and provided significant cost savings.
Offline cementing is not a new concept. In Q4 2015, an operator drilling in the Eagle Ford shale began the movement of their program toward offline cementing of both the surface and production casings. The operator determined that reducing flat time was crucial to create a cost savings (
The service company was able to cement both the surface and intermediate casing strings offline while the operator skidded to the next well to begin rigging up. All surface casings were drilled and cemented offline and the rig skidded back to drill for the intermediate casings, which were also cemented offline. Approximately 15 hours was saved by skidding between surface strings, and another 16 hours was saved between intermediate casings.
This paper discusses the successful use of offline cementing during drilling operations in northeastern Pennsylvania. The flat time reduction achieved during this drilling program can be quantified into a cost savings of approximately USD 80,000 per well.
Underbalanced drilling via air drilling is deeply rooted in the Northeast United States due to its distinct geology, high rates of penetration (ROP) and drilling efficiency, and low cost of circulating material. The active drilling programs of several independent operators in the Marcellus and Utica Basins are well suited for air drilling down to the final kick off point by virtue of competent, stable formations, low static reservoir pressures, and manageable water ingress to the wells. Air drilling provides near-atmospheric pressure at the borehole bottom, since there is no fluid column with resulting hydrostatic pressure. The result is very high ROP with essentially 100% drilling efficiency, allowing the completion of intervals in one or two bit runs. A service company deployed a cross-functional product development team to optimize oilfield air bits for these applications over the last two years, resulting in decreased drilling costs through increased performance and reliability.
The oilfield air drilling environment places unique challenges on drill bit design due to the increased risk of downhole vibrations, corrosion, abrasive wear, heat generation, and seal infiltration of very fine cuttings. The application requirements have increased due to deeper intervals requiring passage through multiple high unconfined compressive strength formations, extended tangent angles, and rising input energy levels. Accordingly, enhancements to both the cutting structures and sealed bearing systems were vigorously pursued. Several cutting structure design iterations were evaluated in both laboratory and field tests. A new sealed bearing system was developed and implemented for increased life and reliability. Modifications to the bit body for stability were included, and the bit hydraulics were further optimized.
Through an understanding of the objectives and application challenges, unique solutions were developed for Northeast oilfield air drilling applications. The optimization process for the new air bit designs is described, and the resulting positive performance metrics are presented. Improvements were observed in distance drilled, ROP, seal effective rate, and dull condition. Lessons learned were also used to refine the recommended drilling parameters and practices through the challenging formations encountered in these tangent sections, which can span in excess of 7000 feet. These enhancements all contributed to reduced drilling cost and days per well, for increased rig productivity.
The natural gas fields throughout the Marcellus and Utica Basins in the Northeast U.S. continue to deliver rising total gas production for the U.S. and the world through increased capacities in pipelines and LNG trains. Improved drilling performance as documented in this paper are driving continuous improvement in the overall upstream drilling economics of the region.
Gowida, Ahmed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Formation density plays a central role to identify the types of downhole formations. It is measured in the field using density logging tool either via logging while drilling (LWD) or more commonly by wireline logging, after the formations have been drilled, because of operational limitations during the drilling process that prevent the immediate acquisition of formation density.
The objective of this study is to develop a predictive tool for estimating the formation bulk density (RHOB) while drilling using artificial neural networks (ANN). The ANN model uses the drilling mechanical parameters as inputs and petrophysical well-log data for RHOB as outputs. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), standpipe pressure (SPP) and rotating speed (RPM), are measured in real time during drilling operation and significantly affected by the formation types. A dataset of 2,400 data points obtained from horizontal wells was used for training the ANN model. The obtained dataset has been divided into a 70:30 ratio for training and testing the model, respectively.
The results showed a high match with a correlation coefficient (R) between the predicted and the measured RHOB of 0.95 and an average absolute percentage error (AAPE) of 0.71%. These results demonstrated the ability of the developed ANN model to predict RHOB while drilling based on the drilling mechanical parameters using an accurate and low-cost tool. The black-box mode of the developed ANN model was converted into white-box mode by extracting a new ANN-based correlation to calculate RHOB directly without the need to run the ANN model. The new model can help geologists to identify the formations while drilling. Also, by tracking the RHOB trends obtained from the model it helps drilling engineers avoid many interrupting problems by detecting hazardous formations, such as overpressured zones, and identifying the well path, especially while drilling horizontal sections. In addition, the continuous profile of RHOB obtained from the developed ANN model can be used as a reference to solve the problem of missing and false logging data.
Operators face the continuing challenge to improve drilling efficiency for cost containment, especially in deepwater drilling environments where drilling costs are significantly higher. Innovative drilling technologies have been developed and implemented continuously to support the initiative. In many areas of the world, including the Gulf of Mexico (GOM), hydrocarbon reservoirs exist below thick non-porous and impermeable sequences of salt that are considered a perfect cap rock. However, salt poses varied levels of drilling challenges due to its unique mechanical properties.
At ambient conditions, the unconfined compressive strength (UCS) of salt varies between 3,000 to 5,000 psi; however, the strain at failure for salt can be an order of magnitude higher when compared to other rocks. Consequently, during drilling salt's viscoelastic behavior requires that its must be broken with an inter-crystalline or trans-crystalline grain boundary breakage. When compared to other rock types, the unique isotropic nature of salt results in a level of strain that is much higher for the given elastic moduli. This strain level makes salt failure mechanics different from other rock types that are prevalent in the GOM.
Hybrid bits combine roller-cone and polycrystalline diamond compact (PDC) cutting elements to perform a simultaneous on-bottom crushing / gouging and shearing action. Two divergent cutting mechanics pre-stresses the rock and apply high strain for deformation and displacement, resulting in highly efficient cutting mechanics. To meet the drilling objectives, different hybrid designs have been implemented to combine stability and aggressiveness for improved drilling efficiency. An operator, while drilling salt sections at record penetration rates, has successfully used this innovative process of rock failure utilizing the dual-cutting mechanics of hybrid bits. This has resulted in significant value additions for the operator.
This paper analyzes field-drilling data from successful GOM wells and attempts to correlate salt failure mechanics and provide insight into dual-cutting mechanics and its correlation with salt failure. The paper also reviews the drilling mechanics of hybrid bits in salt and highlights importance of dual-cutting mechanics for achieving higher penetration rates in salt through improved drilling efficiency.
Today, almost half of Western Canada's natural-gas production comes from the Triassic-aged Montney formation, a sixfold increase over the last 10 years while gas production from most other plays has declined. In the last few years, demand for condensate as diluent for shipping bitumen has driven development of liquids-rich Montney natural gas leading to a surge in gas production and gas-on-gas competition in the Western Canadian Sedimentary Basin (WCSB), which has driven local natural gas prices down. This has had a material effect on the operations and finances of companies active in the Western Canada and is reshaping the Canadian gas industry. A significant portion of this growth has taken place in NE British Columbia and with the planned electrification of the industry in British Columbia, including the nascent LNG operations, will influence tomorrow's power industry in this region. NE British Columbia is a geographically large area with sparse population and the power supply into this region has lagged behind development of oil and natural gas resources. The area was originally served from geographically closer NW Alberta. More recently, supply was established from the BC Hydro power grid with the most significant developments being Dawson Creek-Chetwynd Area Transmission (DCAT) completed in 2016 and the additional 230 kV transmission projects scheduled for completion in 2021.
An operator and rig contractor have been implementing drilling operations automation (DOA) pursuing well design and drilling operational execution improvements in terms of safety, quality, delivery, and cost (SQDC). Today, drilling automation enables tighter process control of operations and well design stakeholders are beginning to fully understand and anticipate its value.
DOA requires applying a process control approach and defining well construction processes at a very detailed level. This process control approach is proposed as a method to study and improve work steps and integrate them into overall operational activities. Optimizing, much less controlling, a drilling system is a difficult task with a multitude of variables to manage. The process of automating operations may be one of the best tools to reduce the number of unknown variables and better deliver consistent SQDC results.
Automation case studies such as a downhole Weight on Bit (WOB) drilling system, a directional drilling advisory system, a sliding system for conventional steerable mud motors, and an integrated tubular running system are described to highlight the role of automation in assisting operators and contractors to efficiently manage and improve the well construction process. Process automation requires improvements in foundational systems, tools, and data quality to support operational performance. The most significant finding is how automated systems enable operations to be practically managed at a detailed level by drilling personnel, engineers, and other stakeholders. After practices and systems are proven and automated, they can be scaled and managed over an entire rig fleet. This will ultimately enable today's well construction and drilling system related risks to be mitigated and managed, leading to further SQDC rewards with more efficient well designs.
The operator and rig contractor will share perspectives for realizing value and opportunity through applying DOA. Experience shows DOA-influenced standardized operations can result in eliminating steps that are no longer needed. Automation enables changes to well design that are just beginning to be understood and anticipated by drilling teams. The challenge will be linking these opportunities to pursue new capabilities supporting well design improvement. This will be the true benefit from automating drilling operations.
Antipova, Ksenia (Skolkovo Institute of Science and Technology, Digital Petroleum) | Klyuchnikov, Nikita (Skolkovo Institute of Science and Technology, Digital Petroleum) | Zaytsev, Alexey (Skolkovo Institute of Science and Technology, Digital Petroleum) | Gurina, Ekaterina (Skolkovo Institute of Science and Technology, Digital Petroleum) | Romanenkova, Evgenia (Skolkovo Institute of Science and Technology, Digital Petroleum) | Koroteev, Dmitry (Skolkovo Institute of Science and Technology, Digital Petroleum)
Majority of the accidents while drilling have a number of premonitory symptoms notable during continuous drilling support. Experts can usually recognize such symptoms, however, we are not aware of any system that can do this job automatically. We have developed a Machine learning algorithm which allows detecting anomalies using the drilling support data (drilling telemetry). The algorithm automatically extracts patterns of premonitory symptoms and then recognizes them during drilling.
The machine learning model is based on Gradient Boosting decision trees. The model analyzes real time drilling parameters within a sliding 4-hour window. For each measurement, the model calculates the probability of an accident and warns about anomaly of particular type, if the probability exceeds the selected threshold.
Our training sample comes from 20+ oilfields and consists of sections related to 80+ accidents of the following types: stuck pipe, mud loss, gas-oil-water show, washout of pipe string, failure of drilling tool, packing formation, that occurred while drilling, trip-in, trip-out, reaming; the sample also includes more than 700 sections without accidents.
We have designed the prediction model to work during drilling new wells and to distinguish the normal drilling process from the faulty one. One can configure the anomaly threshold to balance amount of false alarms and the number of missed accidents.
To evaluate quality of the model we measure data science metrics and check an industry-driven criterion. The model can identify 40 accidents from about 80 with high confidence, whereas for the others there is still a room for improvement. Our findings suggest that including more accidents of underrepresented types will improve quality. Other data science metrics also support aptitude of the model. Finally, having data from multiple heterogeneous oilfields, we expect that the model will generalize well to new ones.
This paper presents a good practice of development and implementation of a data-driven model for automatic supervision of continuous drilling. In particular, the model described in the paper will assist specialists with drilling accidents prediction, optimize their work with data and reduce the nonproductive time associated with the accidents by up to 20%.
In 2016, Malaysia Petroleum Management (MPM), the regulatory body of PETRONAS launched a 3 year dedicated strategy to intensify the idle wells restoration and production enhancement activities in order to maximize profitability through efficiency and success rate improvement. The basis of this strategy is the risk-sharing integrated operations in which the industry embraced it in all major well intervention activities. As the drilling activities dropped drastically over the past few years, it was crucial that the well intervention activities are carried out with high efficiency and success rate to restore the production.
The strategy went through various development changes throughout the 3 year journey. As the well intervention scope covers a wide range of activities, the framework of this integrated risk sharing mechanism provided the flexibility that is required for the execution of the various scopes and meet specific value targets either profitability from production gain or cost saving from decommissioning and infill drilling. Each of the project carried unique Key Performance Indicators (KPIs) as the guiding principles to drive the efficiency improvement that was required. A unique process called Total Wells Management (TWM) was implemented as the overlaying guide to further improve the uncertainty of subsurface challenges, operation optimization and commercial risk exposure.
This paper outlines the overall post mortem analysis of the 22 projects that were executed under this integrated operations strategy between MPM, ten operators and five main service companies. This strategy, known to the industry as the Integrated Idle Wells Restoration (IIWR) program, has become the new norm on how well intervention and subsurface assessments are executed to yield the best results especially in late life fields. The risk sharing integrated framework have proven to be a win-win scenario for all involved parties. The scope was also extended to cover non production adding activities such as wells decommissioning, well startups and pre drilling zonal isolation. IIWR have also opened up the opportunities for many ‘first in Malaysia’ projects such as the first subsea hydraulic intervention, first subsea decommissioning and also the reinstatement of technologies such as coiled tubing catenary. The biggest impact from this 3 years strategy implementation can be seen from the Unit Enhancement Cost (UEC) improvement where the average UEC was reduced from 14 to 17 USD per barrel of oil to about 4 to 7 USD barrel of oil.
Although there were major challenges, the overall results have been very encouraging. This framework is also being replicated for drilling and completion activities as well. Specific to well intervention, this IIWR framework is currently being put through an enhancement process to further expand the landscape of well intervention activities without compromising safety, operational efficiency and business profitability.
Gan, Thomas (Shell Trinidad & Tobago Ltd) | Kumar, Ashok (Shell Trinidad & Tobago Ltd) | Ehiwario, Michael (Shell Exploration & Production Company) | Zhang, Barry (Quantico Energy Solutions) | Sembroski, Charles (Quantico Energy Solutions) | de Jesus, Orlando (Quantico Energy Solutions) | Hoffmann, Olivier (Quantico Energy Solutions) | Metwally, Yasser (Quantico Energy Solutions)
Borehole-log data acquisition accounts for a significant proportion of exploration, appraisal and field development costs. As part of Shell technical competitive scoping, there is an ambition to increase formation evaluation value of information by leveraging drilling and mudlogging data, which traditionally often used in petrophysical or reservoir modelling workflow.
Often data acquisition and formation evaluation for the shallow hole sections (or overburden) are incomplete. Logging-while-drilling (LWD) and/or wireline log data coverage is restricted to mostly GR, RES and mud log information and the quality of the logs varied depending on the vendor companies or year of the acquisition. In addition, reservoir characterization logs typically covered only the final few thousand feet of the wellbore thus preventing a full quantitative petrophysical, geomechanical, geological correlation and geophysical modelling, which caused limited understanding of overburden sections in the drilled locations and geohazards risls assessment.
Use of neural networks (NN) to predict logs is a well-known in Petrophysic discipline and has often used technology since more than last 10 years. However, the NN model seldon utilized the drilling and mudlogging data (due to lack of calibration and inconsistency) and up until now the industry usually used to predict a synthetic log or fill gaps in a log. With the collaboration between Shell and Quantico, the project team develops a plug-in based on a novel artificial intelligence (AI) logs workflow using neural-network to generate synthetic/AI logs from offset wells logs data, drilling and mudlogging data. The AI logs workflow is trialled in Shell Trinidad & Tobago and Gulf of Mexicooffshore fields.
The results of this study indicate the neural network model provides data comparable to that from conventional logging tools over the study area. When comparing the resulting synthetic logs with measured logs, the range of variance is within the expected variance of repeat runs of a conventional logging tool. Cross plots of synthetic versus measured logs indicate a high density of points centralized about the one-to-one line, indicating a robust model with no systematic biases. The QLog approach provides several potential benefits. These include a common framework for producing DTC, DTS, NEU and RHOB logs in one pass from a standard set of drilling, LWD and survey parameters. Since this framework ties together drilling, formation evaluation and geophysical data, the artificial intelligence enhances and possibly enables other petrophysical/QI/rock property analysis that including seismic inversion, high resolution logs, log QC/editing, real-time LWD, drilling optimization and others.
Directional drilling for hydrocarbon exploration has been challenged to become more cost-effective and consistent with fast-growing drilling operations for both offshore and onshore production areas. Autonomous directional drilling provides a solution to these challenges by providing repeatable drilling decisions for accurate well placement, improved borehole quality, and flexibility to adapt smoothly to new technologies for drilling tools and sensors. This work proposes a model predictive control (MPC)-based approach for trajectory tracking in autonomous drilling. Given a well plan, bottomhole assembly (BHA) configuration, and operational drilling parameters, the optimal control problem is formulated to determine steering commands (i.e., tool face and steering ratio) necessary to achieve drilling objectives while satisfying operational constraints. The proposed control method was recently tested and validated during multiple field trials in various drilling basins on two-and three-dimensional (2D and 3D) well plans for both rotary steerable systems (RSS) and mud motors. Multiple curve sections were drilled successfully with automated steering decisions, generating smooth wellbores and maintaining proximity with the given well plan.