bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantifying PDC Bit Wear in Real-Time and Establishing an Effective Bit Pull Criterion Using Surface Sensors
Authors: Witt-Doerring Ysabel , Pastusek Paul Pastusek , Ashok Pradeepkumar , Oort Eric van 
Published: September 2021

Abstract
It is useful during drilling operations to know when bit failure has occurred because this knowledge can be used to improve drilling performance and provides guidance on when to pull out of hole. This paper presents a simple polycrystalline diamond compact (PDC) bit wear indicator and an associated methodology to help quantify wear and failure using real-time surface sensor data and PDC dull images.
The wear indicator is used to identify the point of failure, after which corresponding surface data and dull images can be used to infer the cause of failure. It links rotary speed (RPM) with rate of penetration (ROP) and weight-on-bit (WOB). The term incorporating RPM and ROP represents a "sliding distance", i.e. the number of revolutions required to drill a unit distance of formation, while the WOB represents the formation hardness or contact pressure applied by the formation.
This PDC bit wear metric was applied and validated on a data set comprised of 51 lateral production hole bit runs on 9 wells. Surface electric drilling recorder (EDR) data alongside bit dull photos were used to interpret the relationship between the wear metric and observed PDC wear. All runs were in the same extremely hard (estimated 35 – 50 kpsi unconfined compressive strength) and abrasive shale formation. Sliding drilling time and off-bottom time were filtered from the data, and the median wear metric value for each stand was calculated versus measured hole depth while in rotary mode.
The initial point in time when the bit fails was found to be most often a singular event, after which ROP never recovered. Once damaged, subsequent catastrophic bit failure generally occurred within drilling 1-2 stands. The rapid bit failure observed was attributed to the increased thermal loads seen at the wear flat of the PDC cutter, which accelerate diamond degradation. The wear metric more accurately identifies the point in time (stand being drilled) of failure than the ROP value by itself.
Review of post-run PDC photos show that the final recorded wear metric value can be related to the observed severity of the PDC damage. This information was used to determine a pull criterion to reduce pulling bits that are damaged beyond repair (DBR) and reduce time spent beyond the effective end of life. Pulling bits before DBR status is reached and replacing them increases overall drilling performance.
The presented wear metric is simple and cost-effective to implement, which is important to lower-cost land wells, and requires only real-time surface sensor data. It enables a targeted approach to analyzing PDC bit wear, optimizing drilling performance and establishing effective bit pull criteria.
Four Simple Questions: Decision-Centered Risk and Project Management
Authors: Haskett William 
Published: September 2021

Abstract
A decision-centric approach to projects creates confidence, improves value, and shortens time to revenue. A straight-forward objective based approach to managing project decisions is presented in the form of four primary questions. Those questions are:Does the issue/threat/opportunity make a material difference to the project? (Materiality)Can anything be done to affect the outcome? (Influence)Can you afford to do anything about it? (Value)What if you are wrong? (Confidence)
Materiality – An issue/threat/opportunity must make a material difference to a project decision to be worth receiving attention. The concept of materiality will vary in size and consequence from project to project, so it is important to maintain a decision focus. Understanding the variability in the project with respect to decision thresholds can provide an indication of materiality. We must also ask how different our current assessment of the project, or its environment could get before we would like to change our decision.
Affective ability – Accepted risk-management options of avoidance, mitigation, transfer, and acceptance present the decision options within this category. In considering the consequences the options, decision tools such as Indifference Assessment and Pain and Regret Assessment.
Avoidance, Mitigation, or Transfer – while most projects can benefit through risk reduction, such effort must make economic sense. Risk reduction paths must add value to the project through added upside or elimination of at least a portion of downside threat. The value of these efforts is aided by use of tools such as Value-of-Information, Value-of-Control, and Value-of-learning.
Being wrong – Making a regretful decision is always a possibility but the source of the "wrongness" and its likely impact is often overlooked by teams.
In project planning and execution, decision-makers are often presented with a plethora of issues, threats, and opportunities. From development planning through implementation significant time and resource waste can be cut by prioritizing effort to the issues that matter. Understanding the issues in the context of materiality and then what to do, if anything, about an issue, becomes key to maximizing success. This approach cuts waste and focuses the attention on what matters.
Decision Intelligence not only increases the probability of making the best decisions, but it also prioritizes work to those items that matter either for value or decision path. While most of the decision tools referenced are well documented in the literature, placing them into the context of the Four Question Approach allows teams and management to focus more closely on efficiently mitigating issues, shortening workflow, and creating significantly higher decision confidence. This novel approach works well in all phases of project planning through project management implementation.
An Innovative Artificial Intelligence Framework for Reducing Carbon Footprint in Reservoir Management
Authors: Katterbauer Klemens , Al Sofi Abdulkarim , Marsala Alberto , Yousif Ali 
Published: September 2021

Abstract
The energy industry has been transformed considerably in the last years. Sustainable development of oil and gas reservoir has become a major driver for these energy companies, and strengthened the focus to maximize hydrocarbon extraction while minimizing the associated carbon footprint. The focus has been further on maximizing efficiency and waste reduction in order to enhance profitability of projects. Challenges still remain in terms of that the carbon emissions from oilfield operations, related to the production, disposal and utilization of water and hydrocarbons, may be significant and the objective of increasing production has to be traded off in many instances against the quest for reducing carbon emissions. The fourth industrial revolution has brought new opportunities for companies to enhance decision making in their upstream development and optimize their recovery potential while minimizing the carbon footprint and associated cost.
In this work, we present a smart approach for optimizing recovery while minimizing the carbon footprint of a reservoir in terms of the associated development and production activities. We use an advanced nonlinear autoregressive neural network approach integrated with time-lapse electromagnetic monitoring data to forecast production and carbon emissions from the reservoir in real-time, under uncertainty. The artificial intelligence approach also allows to investigate a circular carbon approach, where the produced greenhouse gases are re-injected into the well, while at the same time adjusting water injection levels. This allows to forecast and analyze the impact of a circular development plan.
We tested the AI framework on a synthetic reservoir encompassing a complex carbonate fracture system and well setup. The carbon emissions were forecasted in real-time based on the previous production rates and the defined injection levels. The forecasted carbon emissions were then integrated into an optimization technique, in order to adjust injection levels to minimize water cut and overall carbon emissions, while optimizing production rates.
Results were promising and highlighted the potential significant reductions in carbon emissions for the studied synthetic reservoir case. Moreover, the deployment of deep electromagnetic surveys was proved particularly beneficial as a deep formation evaluation monitoring method for tracking the injected waterfront inside the reservoir and optimizing the sweep efficiency, while minimizing the inefficient use of water injection. Accordingly, such integrated AI approach has a twofold benefit: maximizing the hydrocarbon productivity, while minimizing the water consumption and associated carbon emissions.
Such framework represents a paradigm shift in reservoir management and improved oil recovery operations under uncertainty. It proposes an innovative integrated methodology to reduce the carbon footprint and attain a real-time efficient circular development plan.
Impact of Hydraulic Fracture Fairway Development in Multi-Stage Horizontal Laterals - A Production Flow Simulation Study
Authors: Khan Abdul Muqtadir , Olson Jon 
Published: September 2021

Abstract
The vast shale gas and tight oil reservoirs cannot be economically developed without multi-stage hydraulic fracture treatments. Owing to the disparity in the density of natural fractures and the different in-situ stress conditions in these formations, micro-seismic fracture mapping has shown that hydraulic fracture treatments develop a range of large-scale fracture networks. The effect of these various fracture geometries on production is a subject matter in question.
The fracture networks approximated with micro-seismic mapping are integrated with a commercial numerical production simulator that discretely models different network structures. Two fracture geometries have been broadly proposed, i.e., orthogonal and transverse. The orthogonal pattern represents a network with cross-cutting fractures orthogonal to each other, whereas transverse profile maps uninterrupted fractures achieving maximum depth of penetration into the reservoir. The response for a single stage is further investigated by comparing the propagation of each stage to be dendritic versus planar. A dendritic propagation is a bifurcation of the induced hydraulic fracture due to the intersection with the natural fracture (failure along the plane of weakness).
For the same injected fracture treatment volume, the transverse network attains a higher penetration into the reservoir, achieves a higher stimulated reservoir volume (SRV), and produces around 40-65% more than the orthogonal network over a timespan of 10 years. The SRV will largely dictate the drainage area in a tight environment. The cumulative production rises until the pressure drawdown reaches the extent of the fracture fairway. For the orthogonal network, the unstimulated reservoir boundary is reached at a sooner time than the transverse network. It is found that by increasing the fracture spacing in both the networks from 100 ft to 150 ft, the relative production was enhanced in the orthogonal network by 41%, but when it was further increased to 200 ft- there was a minor drop (not increase) in the relative production (4.5%). For an infinite conductivity fracture, the width of the fracture has minimal effect on oil and gas production.
For the dendritic pattern, the size of the SRV created due to the interaction between the induced and natural fractures largely depends on the length of natural fractures and the point of interaction (center, off-center, or extremity). Effect of length, distance of natural fracture from wellbore, and the point of interaction is evaluated.
A novel approach for reservoir simulation is used, where porosity (instead of permeability) is used as a scaling parameter for the fracture width. The forward modeling effort, including the comparative fracture geometries setup, induced, and natural fracture interaction parametric study, is unique.
Solid-State Gyro Technology Allows Safe And Reliable Real-Time Remote Operations
Authors: Ledroz Adrián , Smart Barry , Maharaj Navin 
Published: September 2021

Abstract
There are several reasons for obtaining gyroscopic surveys in directional wells. A gyro measurement provides reliable data when magnetic measurements are affected by interference from nearby wells; it can significantly reduce the positional uncertainty and provides redundancy data and gross error checks on MWD surveys. However, the complexity and extent of the necessary testing and handling of the tools have prevented widespread adoption, and gyro services have remained limited to "must-have" scenarios. The benefits of solid-state technology and new developments in communication capabilities are gradually changing the way of thinking related to wellbore positioning.
The first gyro while drilling tools were introduced in the early 2000s and were based on spinning mass gyro technology. These gyros can be very accurate with low noise levels and drift; however, they are fragile, built with moving parts, and susceptible to calibration shifts. Extensive pre-job testing, validation during job execution and post-job analysis are required to obtain reliable directional survey data. Solid-state gyros have reached the same, or even better, levels of noise and drift without the fragility of their spinning mass counterpart.
With different degrees of complexity and coverage, remote operations have been used for many years in the oilfield. Still, the adoption of monitoring gyro services with no personnel at the rig-site has been minimal due to the described complexity of the system and the small volume of jobs that prevented investment and the development of the necessary processes. Solid-state gyro technology addresses these challenges
More than 30 gyro-while-drilling jobs have successfully run remotely. The changes in operational procedures forced by the Covid-19 pandemic accelerated the demand for uncrewed operations, and solid-state gyro technology has shown high reliability with zero non-productive time due to tool failures or shifts in the calibration. This new way of working also results in a significant reduction in the environmental impact of the operations as all travel related to personnel and equipment has been reduced and battery life extended by up to 10.
Several scenarios related to wellbore positioning and directional drilling greatly benefit by having a gyro in the BHA. The gyro technology and the workflow described in this paper show how this can be done reliably, maintaining the quality of the survey data and reducing the environmental impact.
Smart Lost Circulation Materials to Seal Large Fractures
Authors: Tabatabaei Maryam , Dahi Taleghani Arash 
Published: September 2021

Abstract
Lost circulation problems may result in a significant downtime, a considerable reduction of the rate of penetration, or even well control problems. Despite advances in manufacturing lost circulation materials (LCMs), some formations, like heavily fractured carbonates, have complete losses during drilling. We develop smart LCMs using shape memory polymers (SMPs), and program them thermo-mechanically to satisfy size limitations imposed by bottomhole assemblies (BHA). Elevated downhole temperatures act as an external trigger to recover the permanent shape of LCMs, which could expand ten times larger than the temporary (programmed) dimensions for deployment. Smart LCMs are a combination of various material categories such as granular, fibrous (one-dimensional or 1-D) and planar (two-dimensional or 2-D) configurations that resume to the original shape after exposure to high temperatures. The LCMs form different structures such as flatted pellet, disc-shaped, spider-shaped, and spindled, which, respectively, presents grains, 1-D fibers, 2-D stars, and 2-D lattices after recovery. A combination of the above categories attempt to build three-dimensional (3-D) plugging capabilities across various sized fractures.
Extending Production Life of Mature Fields in the North Sea with Multilateral Technology
Authors: Kelsey Matthew , Raaholt Magnus , Einervoll Olav , Nafikov Rustem , Amble Stian 
Published: September 2021

Abstract
Multilateral technology has for nearly three decades extended the production life of fields in the North Sea by delivering a higher recovery factor supported by the cumulative production of the multiple laterals. Additionally, operators continue to look at methods to reduce the environmental impact of drilling and intervention. Taking advantage of the latest multilateral technology can turn otherwise unviable reservoirs into economically sound targets by achieving a longer field life while minimizing construction costs, risk, and environmental impact.
This paper will focus on mature fields in the region that have used multilateral applications for wells that were reaching the end of their life and have been extended to further economic production.
This paper discusses challenges faced to provide a multilateral solution for drilling new lateral legs in existing wells where there is a lack of available slots to drill new wells. Additionally, discussion will cover completion designs that tie new laterals into existing production casing. The case study will include discussion of workover operations, isolation methods, and lateral creation systems.
The paper focuses on the challenges, solutions, and successful case study of a retrofit multilateral well constructed in the North Sea which extended production life in a mature field by using innovative multilateral re-entry methods. The paper also provides insight as to methodology for continually improving reliability of multilateral installations to maximize efficiencies.
Cognitive HSE Risk Prediction and Notification Tool Based on Natural Language Processing
Authors: Danabal Tharunya , Sarah John Neethi , Pramod Ghawade Abhijeet , Padharinath Ahire Pranjal 
Published: September 2021

Abstract
The focus of this work is on developing a cognitive tool that predicts the most frequent HSE hazards with the highest potential severity levels. The tool identifies these risks using a natural language processing algorithm on HSE leading and lagging indicator reports submitted to an oilfield services company’s global HSE reporting system. The purpose of the tool is to prioritize proactive actions and provide focus to raise workforce awareness.
A natural language processing algorithm was developed to identify priority HSE risks based on potential severity levels and frequency of occurrence. The algorithm uses vectorization, compression, and clustering methods to categorize the risks by potential severity and frequency using a formulated risk index methodology. In the pilot study, a user interface was developed to configure the frequency and the number of the prioritized HSE risks that are to be communicated from the tool to those employees who opted to receive the information in a given location.
From this pilot study using data reported in the company’s online HSE reporting system, the algorithm successfully identified five priority HSE risks across different hazard categories based on the risk index. Using a high volume of reporting data, the risk index factored multiple coefficients such as severity levels, frequency and cluster tightness to prioritize the HSE risks. The observations at each stage of the developed algorithm are as follows:In the data cleaning stage, all stop words (such as a, and, the) were removed, followed by tokenization to divide text in the HSE reports into tokens and remove punctuation.In the vectorization stage, many vectors were formed using the Term Frequency - Inverse Document Frequency (TF-IDF) method.In the compression stage, an autoencoder removed the noise from the input data.In the agglomerative clustering stage, HSE reports with similar words were grouped into clusters and the number of clusters generated per category were in the range of three to five.
The novelty of this approach is its ability to prioritize a location’s HSE risks using an algorithm containing natural language processing techniques. This cognitive tool treats reported HSE information as data to identify and flag priority HSE risks factoring in the frequency of similar reports and their associated severity levels. The proof of concept has demonstrated the potential ability of the tool. The next stage would be to test predictive capabilities for injury prevention.
Modeling of Compositional Grading in Heavy Oil Fields
Authors: Vinhal Andre , Azeem Jawad , Pedersen Karen 
Published: September 2021

Abstract
Field data for the compositional variation with depth in five petroleum reservoirs have been analyzed including one rich in asphaltenes with a very high compositional gradient. The data follows the well-known pattern with an increasing concentration of heavy components with depth. The observed compositional variation is higher than what could be explained by gravity segregation alone. An enthalpy term was added to take into account that the components have different preferences for residing at a higher or a lower temperature. It was found necessary to modify the enthalpy term to account for the impact of fluid viscosity and concentration of aromatic components on the relative rate at which smaller and larger molecules move in a fluid column. With this modification, a good match was seen of the compositional variation in the five reservoirs initially analyzed. The method was successfully tested on three other reservoirs, not part of the initial analysis, one of which had a fluid column of 970 m.