Field development strategies in unconventional shale reservoirs have increased in intensity over the last few decades. Completion design and well spacing have been key focus variables in the incremental design process. With this wide range of design and development strategies, assets across different basins might end up with wells from a variety of design generations. This could make type curve creation even more complicated as it does not account for impact of hydrocarbon drainage in an area by the older (parent) well on the newer (child) wells. The present paper tackles this issue by addressing type curve development by including date dependent spacing variables to account for the dynamism of field development strategies over the years.
The present paper analyzes the impact of well spacing on type curve development in an asset. Type curve generation is a critical component in evaluation and subsequent planning so de-risking this step is very valuable. A lot of the analysis done in recent years is by considering well spacing as a static variable. The present analysis looks at spacing as a dynamic variable instead to account for time-series based variations. The spacing in the estimation process is also a 3-D spacing algorithm which identifies multiple points along the lateral section of the wellbore for a true evaluation of pressure transient propagation.
The present analysis showed the impact of date dependent well spacing on type curve development. The underestimation of well spacing in well-developed acreages was brought to attention as spacing mean deviations of upto 0.7 Standard Deviation were found between current well speacing and date-dependent well spacing scenarios analyzed. These deviations led to the type curves having upto a 40% EUR differential between estimation processes, with PV10 differentials higher than 100% in some cases. While the degree of impact of time series well spacing varied across the assets evaluated, quantifying the risk in type curve development and subsequent EUR estimation were key conclusions from the analysis.
The present paper presents a novel approach in tackling type curve development for parent and child wells observed across different basins. The paper provides guidelines on creating highly accurate type curves and highlights errors that may arise due to high well density and inter-well interaction by conducting the analysis in the high well density Middle Bakken formation.
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.
Pre-set or off-depth composite plugs can cause significant non-productive time for a well operator. In the past, fracturing operations using a composite frac or bridge plug that has been pre-set or set off depth required a coiled tubing unit or workover rig to drill the plug out. Then, the well operator could resume the fracturing job or access the wellbore below the plug. However, as this paper demonstrates, composite plug milling via wireline using a tractor and a tractor-based milling tool is a faster, safer, and more cost-effective solution.
In a shale well located in the northern panhandle of West Virginia, a composite frac plug was set off- depth. Prior to mobilizing the tractor-based solution to location, the operator attempted pumping approximately 60,000 pounds of sand to sand-cut the off-depth frac plug out of the well. The sand cutting, though, did not work because perforations above the frac plug took the sand. Other tubing-based solutions required more mobilization time and complex logistics for rigging down and/or moving equipment on location. Therefore, the operator chose a wireline-based method for ease of operation, reduced HSE risk, and cost savings.
The tractor took 50 minutes to drive down 1718 ft in the lateral to the plug. The milling tool milled the top slips on the frac plug in approximately nine hours, and the tractor then pushed the plug 222 ft downhole on top of the previous frac plug. The total time rigged up on the well was 14 hours, and the total time on location was 18 hours. Although this wireline-based plug-milling method takes several hours to mill a plug, the rig-up and execution is simpler than conventional methods, and associated HSE risks on the wellsite are greatly reduced.
The ability to effectively release plugs via wireline provides well operators with another option to complete their objectives, especially when tubing-based methods often take many days or weeks to mobilize at substantial cost to operators.
Singh, Ajay (Anadarko Petroleum Corporation) | Sankaran, Sathish (Anadarko Petroleum Corporation) | Ambre, Sachin (Anadarko Petroleum Corporation) | Srikonda, Rohit (Kongsberg Digital Inc.) | Houston, Zach (Kongsberg Digital Inc.)
Deepwater oil and gas facilities typically encounter on an average up to 5% annual production losses due to unplanned downtime, conservatively estimated at billions of dollars impact for the industry. The existing toolkit and systems in place are not always adequate to identify and predict abnormal events that could lead towards unplanned facility shutdown. The interaction amongst process sub-systems and disturbances that propagate across these sub-systems with changing operating conditions are hard to predict without a fit-for-purpose model (or a digital twin). The focus of current work is on deepwater facility having several oil export pipeline pumps in parallel and several gas compressors in series. The alarm database showed records of several unplanned shutdown events around these critical equipements that resulted in undesirable outcomes such as production deferment, complete facility shutdown, loss of sales volumes and increased operational costs. In this work, an intelligent prognostic solution is proposed using machine learning (ML) framework for automatic prediction of impending facility downtime, and identification of key causative process variables. A systematic workflow was developed to identify, cleanse and process real time data for both model training and prediction. Several ML methods were evaluated; anomaly detection based on Principal Component Analysis (PCA) and Autoencoder (AE) algorithms were found performing better for the type of data available for the deepwater facility. The ML framework also supported analysis of underlying downtime causes to propose suitable mitigation steps. Knowledge based on physical understanding of the process was used to select each sub-system boundary and sensor list on which ML model was trained. These models were then cross-validated to test the accuracy of trained models. Finally, the alarm database was used to confirm the accuracy of the machine leaning models and identify root-causes for unplanned shutdowns. If the operating condition changes over time, the anomaly detection based ML models were setup to adapt to changing conditions by automatic model updates, resulting in significant reduction in false alarms. The adaptive ML models, when applied to one of the sub-system (with 30 different sensor data), predicted 24 unplanned events in 6 months of period, while when applied to another sub-system (with 40 sensor data), predicted only 6 unplanned downtime events. Several predictions were found as early as 30 mins to 2 hours, providing adequate early warning to take proactive actions. Case studies shown in the paper present diagnostic charts and identified early indicators were found in agreement with pre-alarms generated by existing alarm system, thus validating the ML solution. Current toolkit available to identify anomalous process behavior is limited to exception based surveillance with fixed min-max limits on each sensor data. Therefore, proposed adaptive ML solution has shown potential to revolutionize the topside process surveillance. This paper also describes how the ML framework can be scaled for a sustainable solution that provides prediction every minute, keeps the model evergreen utilizing cloud-based model deployment platform to train, predict and trigger automatic model updates and also span multiple process systems and facilities. Finally, we present directions for future work, where the current model can keep predicting various events and over time when sufficient events are collected, more advanced machine learning methods based on supervised ML can be developed and deployed.
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.
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.
Production and drilling activities in offshore installation are one of the most necessary activities of human society. To drill a subsea well and raise the crude oil to a platform, by itself, presents a series of risks. Associated with this activity, when the crude oil reaches the topside of the platform, there are a number of operations that prepare the oil and gas to be exported to land by pipelines or oil tanker vessels, which involves equipment and process that take high temperatures, high pressure and high flow rates. Understanding the dynamics of the factors that can affect the interaction of operators with all these offshore complex systems is critical, because the loss of control of these systems can cause serious accidents, resulting in injuries to workers, environmental damage, loss of production and geopolitical crises. Accidents in the oil and gas offshore installations, such as drilling rigs and FPSOs, can have tragic consequences and all efforts should be targeted to prevent its recurrence. Therefore, from the perspective of current technological developments, it is essential to consider the influence of Human Factors in the risk management of offshore industrial plants.
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.
Ghanavati, Mohsen (Global New Petro Tec Corp.) | Volkov, Maxim (TGT Oilfield Services) | Nagimov, Vener (TGT Oilfield Services) | Ali Mohammadi, Hamzeh (University of Calgary, Global New Petro Tec Corp.)
Production casings of Cyclic Steam Stimulation (CCS) or steam-assisted gravity drainage wells are exposed to significant temperature variations which in many cases resulted in casing breaks in the weakest part which are typically connection joints. The paper focuses on the new downhole logging approach, in monitoring and detecting production casing connection breaks through tubing without requirement for tubing retrieval.
The metal well barriers can be assessed by utilizing electromagnetic (EM) pulse defectoscopy. This is done by running multiple coaxial sensors downhole in tandem. Each sensor generates EM pulse and then records EM decay from surrounding metal tubes. Modeling of recorded EM decay enables precise assessment of metal loss or metal gain in up to four concentric barriers. However, the tool had never been used previously to detect minor defect features as casing breaks through the tubing. To identify casing breaks several yard and field tests have been conducted and new methodologies were developed. The last one included the recognition of specific patterns of raw EM responses, analysis of hole sensors and utilization of data from all coaxial sensors utilized during the downhole survey.
The new approach including downhole EM pulse tools and new data analysis have been implemented to detect casing connection breaks in over a hundred Cyclic Steam Stimulation (CCS) and SteamAssisted Gravity Drainage (SAGD) wells. The paper demonstrates the testing of the application feasibility in a comprehensive yard test and extends to real field examples. All detected breaks were confirmed after tubing removal and were successfully repaired. Paper highlights detection challenges due to different casing connection break types: minor breaks, partial breaks (contrary to fully circumferential), and casing breaks aligned with tubing connections. The technology has helped Operators to fulfil the objectives of connection break detection without tubing removal through a non-intrusive, safe, quick and economical approach.
Today, CSS and SAGD Operators should confirm casing integrity repeatedly prior to each subsequent steam cycle through the time and resource consuming approach of tubing removal and checking the casing integrity mechanically. Utilizing through tubing electromagnetic diagnostics, enables Operators to pick up multiple casing connection breaks in a single run without tubing retrieval.
It is well known that geophysics, particularly the