bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trusted Data as a Company Asset
Authors: Flichy Philippe 
Published: September 2015

Abstract
Digital Oil Field's (DOF) practice's original promise was to gather data to help make better technical and business decisions. Some argue that a required step to enable a shift in the adoption of the DOF methodology is to treat data as an asset. Meaning data as an asset is as valuable as a physical asset. It should be maintained, protected, and respected in its own way with the same diligence we have for a field.
Here we are presenting why data is an asset.
With the advent of analytics, thanks to complex correlations “data can talk”; for it to be meaningful, data needs to be up to date, validated, and standardized so everyone can have access to a single version of the truth. Data is accepted and trusted if the process by which it has been validated is viewable and auditable. Enhancement and manipulations of this data need to be shared and stored centrally, not locally. The logic applied for its manipulation needs to be documented as well.
Trusted data means that each member of the team trusts each other's methodology to transform data into information. An enabler is the generation of meta-data to encapsulate this logic with the shared output.
Such fresh approaches to data validation and sharing will generate significant savings by eliminating duplicate efforts, increasing access time to information, and confidence in using the latest available source of information.
Ensuring this seamless access to information requires identifying where the sources of data are and developing aggregators to make it accessible to all in a standard manner from a single point. Creating this bridge to scattered data from multiple sources over many vendors is a complex task requiring domain expertise from a very heterogeneous set of people… It requires data ownership identification and bridging inconsistencies in data types and quality.
From the capture of data points and its validation to its refining and by-products, “Data as an asset” requires custodians of the integrity of the data, its storage and accessibility. “Data as an asset” means data is valuable and therefore needs to be protected from being misused or stolen. The fact we now “drink from the source” of data (getting data almost directly from the sensors) means we have to secure its acquisition and transmission very thoroughly and properly manage entitlement.
“Data as an asset” and the corollary technical data management it implies, will contribute to maximizing production and reserves; while also reducing costs by applying efficiencies, and connecting the field to the back office.
Leveraging Intelligent-Field Data for Full Field Pressure Transient Analysis
Authors: Al-Wehaibi B.. , BinAkresh S.. , Issaka M.. , Al-Shamrani S.. 
Published: September 2015

Abstract
The last several years have seen an explosion in the number new fields and increments being equipped with Intelligent Field technology, which includes remote monitoring and control of wells in real-time, as well as the acquisition of high frequency pressure and rate data from permanent downhole monitoring systems (PDHMS). This paper shows how this Intelligent Field data is being leveraged to obtain full-field reservoir characterization using both analytical and numerical pressure transient analysis methods. Utilizing data from a Saudi Aramco “intelligent” field, collected over a seven-month period, the paper employs pressure transient analysis to investigate the presence of reservoir boundaries and heterogeneities, and to obtain reservoir and well properties. This work reveals the importance of Intelligent Field data, how it can add value for the pressure trainset analysis, and how it is essential in the multiwell interpretation technique to yield accurate analysis. Major heterogeneity in the reservoir of this field was successfully detected and analyzed using the numerical multiwell interpretation technique, made available by Intelligent Field technology. The results are also compared with pressure transient analysis results from wireline gauge data collected through traditional buildup tests conducted on the same wells, to illustrate the distinct advantages of using Intelligent Field data over traditional wireline test data.
Seismic While-Drilling, Real-Data Reposition the Well in the Seismic Volume and Improve the Accuracy of the Depth Prognosis of the Target Events
Authors: El Tokhy M.. , Fahmy A.. , El marakeby H.. , Cox M.. , Okasha M.. , Khattab H. M. , El Hosary A.. 
Published: September 2015

Abstract
Recording VSP data on wireline is a routine operation and its benefits and short comings are well understood. Recording a similar dataset while drilling allowing Real Time processing of the Seismic traces has several potential advantages in terms of time saving and decision making for the client without the need to pull the pipe out of the hole.
The Baker Hughes Seismic While tool was deployed in highly deviated well in Mediterranian Sea to acquire VSP traces from a Vertical Incidence VSP configuration (Source above receiver). The data is both recorded in memory and a trace extract transmitted to surface using the mud pulse system for real time QC and processing
LEAN Methodology in a Long Term Velocity String Campaign, a Step Forward in Snubbing Operations
Authors: Romero Javier , Janusz Norbert , Farei Ibrahim 
Published: September 2015

Abstract
LEAN concepts have been applied in a wide range of industrial areas to identify and eliminate the waste in every stage of different processes, currently improve efficiency and lower production costs has become popular among Oil and Gas senior-level management. Minimizing waste is the base on which LEAN concepts are built. A LEAN endeavor seeks to eliminate activities or processes that consume resources, add cost or require unproductive time without creating value. The concepts can be described as striving for excellence in operations in which each employee seeks to eliminate waste and participates in the smooth flow of value to the customer. As part of a complete new strategy to ensure permanent improvements within the whole operational environment Petroleum Development Oman (PDO) has applied LEAN on drilling and other corporate core areas. This paper presents a LEAN application to optimize the Snubbing Interventions in order to ensure a significant CAPEX and OPEX reduction in an extensive deliquification campaign for gas wells thru the implementation of high end Velocity String (VS) completions.
The projects involve the application of LEAN methodology from the early planning stages using in many cases re-engineering to the functional specifications, material selection and operational procedures. This re-engineering includes a deep review of every intervention activity, the application of new technologies and the analysis of major issues during previous snubbing interventions, followed by the measurement of the real operational times and deep technical brainstorming on every stage of the intervention process. All those factors have contributed with a significant amount of improvements along the intervention campaign.
Using Trees, Bagging, and Random Forests to Predict Rate of Penetration During Drilling
Authors: Hegde Chiranth , Wallace Scott , Gray Ken 
Published: September 2015

Abstract
Predicting rate of penetration (ROP) has always been of fundamental interest to the drilling industry. Early predictions can assist the engineer in changing parameters to reduce non-productive time (NPT) and achieve optimum ROP. This paper illustrates methods to predict the ROP in a computationally efficient manner using only data available at the surface. These methods can then be incorporated into real time drilling operations, first through a passive diagnostic tool, and then an integrated real-time control loop.
In this work, statistical learning techniques such as trees, bagged trees, and random forests (RF) are used to predict ROP. Trees provide easy interpretability and hence are favored over other non-linear techniques. However, accuracy is imperative in this procedure. Accuracy can be increased by using bootstrap aggregating (bagging) or Random Forests. These techniques are applied, using the statistical software computing package R and its numerous libraries. Statistical learning techniques have been applied to a data set which had nine predictors.
Applying trees to a data set yields great visualization of the data, but the lack of accuracy and can result in substantial overfitting. This shortcoming was rectified using bagging or RF methods to substantially increase accuracy. The results were promising in all cases and acceptable for real time predictions.
Scalability is another concern for real time operations. Computational efficiency of the methods were evaluated, and the best method was based on a combination of computational efficiency and accuracy. Potential time savings which would result from applying the model in real-time optimizations and demonstration of the power of machine learning techniques are included in this paper. Future improvements will be incorporated in real-time prediction during drilling.
State of the art statistical learning and machine learning techniques are applied to prediction of ROP, whereas previous prediction methods have not been based on real-time drilling data. The result is a computationally efficient model which can determine the right features for prediction at each step, while also incorporating engineering judgement and maintaining integrity of the statistical principles being employed. These methods can easily be extended to other drilling parameters such as MSE or Torque and Drag.
Integrated Operations on Offshore Formation Testing
Authors: Wang X.. , Xiao D.. , Dai Y.. , Lei G.. , Coskun S. B. , Fang Y.. 
Published: September 2015

Abstract
Integrated operation (IO), the technologies used to enable distributed collaboration, was introduced to the petroleum industry many years ago, and is being successfully used for production optimization and real-time drilling operation. Previous operations prove it adds value to the industry.
The paper presents a workflow to develop and implement IO on offshore formation testing, which is formation pressure measurement and fluid sampling. The nature of offshore formation testing requires fast and effective decision-making during operation. And each decision is invaluable in the operation process. This paper is to give an answer of how to maximize the value of formation testing by IO.
Offshore formation testing brings together individuals from various disciplines, locations (offshore/onshore) and organizations (oil company/service supplier). The decision-making calls for contributions from all individuals. More importantly, the decision depends on real-time updated data which is measured and monitored from test. These features of formation testing coincide with the application of IO theory, which focuses on cooperation across disciplines using real-time information.
This paper describes the specializations of offshore formation testing operation, and lists the top benefits by the implementation of IO. Experiences are learned from other offshore-related disciplines in the petroleum industry. An optimal workflow to improve the decision-making process in formation testing is suggested. Furthermore, the responsibilities of individuals who are involved in formation testing are defined. At the end, we illustrate an example of the application of this model.
Multifaceted Simulation facilitate Drilling Performance analysis
Authors: Hani Ibrahim , Brahosky Daniel 
Published: September 2015

Abstract
This paper describes the procedures and steps implemented in the introduction and the instigation of a Drilling Performance Analysis Workflow. The task of drilling performance or offset analysis is paramount to the understanding and enactment of a successful drilling campaign, conversely in order to gain exceptional knowledge and profound understanding of problems root cause and the interdependency of the individual relationships a more comprehensive methodology has to be employed.
An Operator experienced several BHA failures in multiple and sequential runs that generated significant non productive time NPT within the multimillion dollar operation. Traditionally, the root cause analysis of a problem follows a certain path involving confirmation by the equipment manufacturing centre that the tools were prepared to specifications, while in parallel the drilling engineers scrutinize the environment where the tools were used and attempt to identify the culprit(s). The conclusion may combine the two findings, nonetheless our observation was that this is not generally the dominant outcome.
Hence, we embarked on developing a unique multifaceted workflow to tackle these inadequacies utilizing several 3-D and 4-D simulation and analysis tools. Individual workflows were established focusing on understanding the interrelated events and using 3D/ 4D modeling tools to relate events in the context of the environment and the interdependency of the BHA behavior and the milieu including exacerbating weather conditions. The software technology integrates data from various databases and organizes it in ways that aid visualization. Any particular areas of interest can be further examined thoroughly looking simultaneously at the geological references, telectric logging data, drilling parameters, and weather conditions all at a glance. The unique arrangement allows offset data from previous wells to be examined concurrently while the subject well is being drilled to assess the timeline of the issue or the recurring possibilities.
Utilizing the software to gather the information and assemble it in a manner that allow the engineers to spend more time analyzing the outcome rather than gathering and arranging data is a significant achievement on its own. The benefits extend to create a system of ownership with accountability for all involved personnel at the base who prepare the tools, at the office who plan and follow up the daily operations, and those who execute the plans at the wellsite.
The approch was proven to add significant value, with the next two well section drilled without any issues avoiding and improving the operator's time versus the planned authority for expenditure (AFE).
Saudi Aramco Real-Time Drilling Operation Activity Recognition & Analysis Engine
Authors: Khudiri Musab , Contreras William , Sanie Faisal , Jovin Tony , Hickin Gary , Kashif Muhammad 
Published: September 2015

Abstract
In recent years, the potential value of real-time drilling data has been well documented and applied across the globe within the Oil & Gas Industry. The information is traditionally used for timely and effective decision-making for drilling operations. Real-Time work processes has helped the industry by saving millions of dollars and mitigating the occurrence of critical hole problems. Drilling departments capitalize on the benefits of real-time data even in the planning stages of a well in order to prepare detailed drilling programs based on the history of any nearby wells. The easy access of real-time active and historical data assists the avoidance of incidents based on the witnessed reaction of the formation during the drilling process.
In order to do more with the data obtained, further analysis is required on the surface data to calculate the witnessed performance for certain activities on the rig site. The first step is to analyze surface data to prepare an operational recognition system. To automatically recognize drilling activities, an algorithm was developed that uses therealtime surface parameters data to calculate the current operation state, this is based on a 2-level activity classification hierarchy. The second step is then to use the results of the operational recognition system to determine key performance indictors based on the witnessed operations. It is therefore key that both calculated methods are as accurate as possible.
This paper will describe the business justification and the process formulated to generate meaningful KPI's for Saudi Aramco to allow for benchmarking and the sharing of best practices.
A System for Real-Time Drilling Performance Optimization and Automation Based on Statistical Learning Methods
Authors: Wallace Scott P. , Hegde Chiranth M. , Gray K. E. 
Published: September 2015

Abstract
This paper introduces a new system to optimize rate of penetration (ROP) while drilling by applying statistical learning methods for ROP prediction and providing real-time performance increases and closed-loop control. This paper establishes the viability of such a method, and then goes on to present a framework for phased development and integration into drilling rig operations. By developing such a system, the authors hope to provide a tool to the oil industry which can improve drilling efficiency, reduce non-productive time (NPT), and lower the cost of drilling.
The statistical model at the core of this system relies on complex machine learning techniques like random forests, neural networks and ensemble techniques to build a predictive performance model of the well from both real-time data and data from previously drilled offset wells. Each rock formation type is analyzed separately, allowing the model to anticipate ROP performance changes at formation boundaries. This model can then assess the controllable drilling parameters and determine whether there is an opportunity to increase ROP by adjusting these parameters to some other value.
The necessary real-time drilling data can be accessed on the WITS/WITSML protocol, and a framework for reading these data into our model in real-time was developed. By using parameter selection techniques, parameters which are significant in predicting ROP were identified. Consequently, an advanced statistical model was developed to process the available data into ROP predictions in real-time, and estimate changes on surface controllable parameters such that the optimum ROP could be reached. A three-phase testing and integration path was developed for taking this tool from concept to completion. The first phase is to apply the statistical model to data from wells which have already been drilled and determine the amount of time which could have been saved by optimizing ROP. Then the second phase is to integrate the system with real-time data acquisition and provide a passive indicator of the current well's optimization level with a red-yellow-green traffic light style indicator. The final stage is to implement closed-loop control, allowing the model to run in a fully automated state. Throughout this implementation, it was shown that there was substantial potential for ROP improvement by following the suggestions of this statistical model.
This concept is innovative as it relies only on data which can be measured in real-time on the rig floor, and does not require information about rock strength, formation drillability, or other downhole properties. This paper provides the path for integrating this new statistical drilling optimization model into drilling operations, and provides a blueprint to bring this concept from simulation to the field.
An Innovative Approach to Formation Evaluation with a Sourceless LWD Technology, A Case study from Partitioned Zone between Kuwait and Saudi Arabia
Authors: Al-Rubaiyea Jamal , Al-Bader Ahmad , Al-Houli Meshari , Al-Najim Abdul-aziz , Al-Duwaish Majed , Elsherif Ahmed 
Published: September 2015

Abstract
Logging While Drilling (LWD) tools with natural radioactive sources present risks to drilling and field development teams, due to the safety hazards inherent to the use of natural radioactive sources in the tools. If any natural radioactive source is left in the hole due to drilling conditions, a complex procedure is required to abandon the hole and sidetrack it.
Recently, a new sourceless LWD logging technique (NeoScope), which incorporates Pulsed Neutron Generators (PNG), was introduced to Wafra Joint Operations (JO).
The new technique provides multiple measurements (Resistivity / Density / Neutron / Sigma / Spectroscopy) from a tool located in a single 26 feet long collar. The PNG* uses electrical power generated from the Measurement-While Drilling (MWD) turbine to generate a large cloud of fast neutrons. These fast neutrons interact with the formation atoms to generate formation hydrogen index, Sigma, and spectroscopy data. In addition, the fast neutron interactions generate a secondary gamma ray cloud that can be used to measure formation density (similar to standard density measurement).
The first successful use of this tool in Wafra Joint Operations was in the first time for in South Fuwaris Field. The tool was run in a pilot hole with an inclination of 60°, cutting Ratawi Limestone and Ratawi Oolite reservoirs. The acquired data were excellent compared to conventional LWD data.
The proven success of this new technology provides risk mitigation / elimination for future operations. Extra care should be taken when using natural radioactive sources while drilling. The use of Sourceless LWD technology is a good risk elimination. Proper planning is required to ensure tool availability (only available for 8 ½” hole)
Integrated Subsurface and Drilling Work Environment Enables Geosteering to Maximize Wellbore Placement in Brown Field Development
Authors: Aillud Gary Stefan , David Richard Mohan 
Published: September 2015

Abstract
E&P Operators are facing challenges with the on-going development of brown fields and the increasing need for accurate wellbore placement and geosteering during infill drilling. This is driven by business plans that involve large in fill well drilling program. The planning and execution of these in fill wells will involve the drilling of highly deviated, horizontal and extended reach wells.
Good use has been made historically of the geosteering services provided by contractors in order to support drilling programs. This paper investigate the process of establishing an Integrated Geosteering Environment (IGE) that would enable efficient collaboration between reservoir geologist, petrophysicist, operational geologists, reservoir engineers and drillers engaged in field development. This environment makes use of a real time data to enable the drilling team to reduce risk and reservoir uncertainty while maximizing wellbore contact with the zone of interest. This will translate into reduced drilling time, better well placement and an increase in capital efficiency. The paper primarily focuses on the establishment of the IGE that will enable the development of workflows to optimize the placement of horizontal wellbore to achieve the business objective. The workflows cover predrill planning, drilling execution including 24/7 real time monitoring to steer the well, post well evaluation and update the reservoir model. This is enabled by real time data acquired while drilling along with modelling visualization tools.
Transforming from the current working environment into an Integrated Geosteering Environment combined with a real time Drilling Environment enables effective decision making and increased business agility towards achieving Operator business objectives, increasing oil recovery and sustaining the production capacity of brownfields.
There are several papers in the past addressing collaborative work environment or shared earth model. This paper discusses real time integrated workflow between sub-surface and drilling domains from a geosteering perspective.