bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single Independent Riser: A Cost Efficient Ultra-Deep Water Riser
Authors: Lirola François , Revault Eric , Lunven Jean-François 
Published: April 2019

Abstract
Hydrocarbon fields discovered in ever deeper waters and uncertainties regarding oil prices are driving the need for cost efficient riser concepts tailored for ultra-deep waters. The Single Independent Riser (SIR) is an innovative hybrid riser configuration optimized for Ultra-Deep water field development. This paper will provide a general overview of the SIR with a specific focus on its installation method and the associated tools that were developed.
The SIR is based on a hybrid solution composed, from bottom-up, of a rigid part vertically tensioned by means of distributed or continuous buoyancy and a flexible jumper. This configuration features improved dynamic behavior for the rigid riser section under fatigue and extreme environmental conditions thanks to its compliant shape. The configuration of the flexible jumper ensures displacements of the rigid riser section are significantly decreased compared to that of an SHR. In addition, its design makes it adapted to any water depth beyond 1000 m. The SIR is also inherently safer as it is less sensitive to buoyancy loss than a riser tensioned by mean of a buoyancy tank. This design is highly versatile and can easily be staggered to comply with design constraints, congested lay out and installation scheme.
The relevancy of the SIR has been assessed on the basis of several case studies derived from actual projects as well as on-coming prospects. The considered cases include a wide range of environments such as West of Africa, East of Africa, Brazil and riser types, both for pipe in pipe and single pipe arrangements, in order to confirm the suitability of the design. Extreme, wave fatigue, VIV fatigue, interference analyses were conducted through these assessments. In parallel, a dedicated installation method was developed to address the specificities of the design and to cover both dry and wet flexible jumper connections. In the frame of this study, specific installation tools were designed to reduce the need for offshore assets during installation.
This paper will first provide a general overview of the SIR, illustrating the arrangement of the various components and providing outcomes of engineering studies confirming the relevancy of the design. A second part will focus on its installation method and the associated tools that were developed.
Innovation and Practice of Geological Modeling Techniques in Complex Clastic Reservoir of Bohai Bay Offshore Oilfield
Authors: Ye Xiaoming , Huo Chunliang , Wang Pengfei , Gao Zhennan , Xu Jing , Mao Yue , Shi Xinlei 
Published: April 2019

Abstract
Bohai oilfield located at Bohai Bay of China mainly consists of complex clastic reservoirs. Reservoir prediction is difficult especially in the deep depth oilfields because of low seismic resolution. In addition, how to quantitatively characterize the risk of new oilfield development and how to build a more elaborate model in the late period of oilfield development are particularly challenging. This paper developed an innovative workflow for complex clastic reservoir characterization of offshore oilfield.
A modeling method based on sedimentary evolution simulation, which can provide modeling constraints, is proposed for deep depth oilfields. Through sedimentary evolution modeling and other information, reservoir and structure uncertainty are analyzed, then reserves scale and reservoir connectivity are evaluated through experimental design, geological modeling and streamline simulation; 3p models are selected lastly. A new method is proposed for building an elaborate model of old oilfield with less grid amounts. The fluid seepage effect caused by some small scale configuration units is characterized by a parameter similar to fault transmissibility multiplier data in numerical simulation model, for there is no actual modeling of the configuration units, so the operation efficiency is greatly improved.
BZ3 oilfield is a deep depth delta oilfield. A geological model was established for exploration evaluation well placement, reserves evaluation and development plan research based on sedimentary evolution simulation and quantitative uncertainty evaluation, the reservoir prediction accuracy is greatly improved that is confirmed by new drilled wells. Through quantitative uncertainty evaluation, reliable geological basis was provided for engineering investment, which can avoid the investment waste caused by geological uncertainty.
Q32 oilfield is a fluvial oilfield that has come into high watercut (86.7%). How to characterize the lateral accretion interlayer (often less than 1 meter) in model for fine remaining oil distribution prediction is difficult, thus the method mentioned above for old oilfield was used. Firstly, a conventional geological model was established, then the lateral accretion interlayer was extracted as interface from it based on configuration results, then the interface was used to extract the parameter named transmissibility multiplier data in the numerical simulation model; a software has been compiled to perform the whole process. Based on the method, more than 100 adjusting wells were disposed and better production results were obtained.
These geological modeling techniques have been widely applied in Bohai Bay offshore oilfields in different periods of oilfield development, including BZ2, JZ2, KL10, CFD6, SZ3, and JZ9. This ensures that these oilfield developed with high quality and efficiency.
Deep Neural Network Based Prediction of Leak-Off Pressure in Offshore Norway
Authors: Choi Jung Chan , Skurtveit Elin , Grande Lars 
Published: April 2019

Abstract
Leak-off pressure (LOP) is an important parameter to determine a weight of drilling mud and in-situ horizontal stresses. When the well pressure become higher than the LOP, it can cause a wellbore instability during drilling, such as a mud loss. Thus, accurate prediction of LOP is important for safe and economical drilling for the oil and gas industry. In this study, we present a novel prediction model for the leak-off pressure (LOP) offshore Norway. The model uses a deep neural network (DNN) applied on a public wellbore database provided by the Norwegian Petroleum Directorate (NPD).
We used a Python-based web scrapping tool to collect data from more than 6400 wells (1800 exploration wells and 4600 development wells) from the NPD factpages. Then, we analyzed the collected data to investigate impacts of spatial and regional factors on the collected LOPs. The DNN model was structured to predict the leak off pressure offshore Norway using open source libraries Keras and Tensor Flow. The model tests have various hidden layers (i.e. 3, 5, and 10 layers). In order to avoid overfitting, we specified an early-stop algorithm. In our study, we took 80% of the data as the training set keeping the remaining 20% to test the model. In total, the database consists of around 3000 leak-off pressure data from about 1800 exploration wells, and grouped in geographical area (North Sea, Norwegian Sea, Barents Sea groups).
The LOPs of the North Sea and the Norwegian Sea show a bi-linear trend with depth. The LOPs that are measured from deeper than 2-3 km below sea level show clear a deviation in trend, with a steeper increase compared to the shallower section. The steeper part of the bi-linear trend at greated sub-surface depths can be related to a coupling with tectonic stresses from base rocks. The data from the Barents Sea shows more scattered LOP compared to the other regions offshore Norway. The scattered data seem to relate to the complex geological history on the Barents sea. In general, the accuracy of the prediction increases with the number of hidden layers. However, when the number of the hidden layer exceed 5, there was no significant improvement in the accuracy of prediction. The validation test shows relatively good prediction of LOP with an MAE (Mean Absolute Error) of less than 0.07 even for areas experiencing complex geological history such as the deep subsurface of the Norwegian Sea and the shallow subsurface of the Barents sea.
This study clearly demonstrates how a data-driven approach combined with machine learning algorithms can provide hidden patterns of not only LOP itself but also the additional information about the lithology, the stress history and the geographical frequency of exploration.
Leveraging Online Analytical Processing Cubes in the World's Largest Offshore and Land Drilling Record System
Authors: Kuwertz Luke , Neill James , Santana Roberta , Skoff Gregory Michael , Steinke Stephen Claude , Williams John , Wolfram Preston , Fink Dave 
Published: April 2019

Abstract
The purpose of this paper is to demonstrate the power and business benefits of leveraging online analytical processing (OLAP) cubes in the utilization of high-level data analytics and data dashboards from an established drilling record system (DRS). The DRS contains over 1.4 million wells, including 75,000 offshore wells drilled worldwide since 1980 with nearly 5 million total bottomhole assembly (BHA) runs from over 100 countries. Since 2009, over 1.5 million BHA runs drilling 2.6 billion feet of formation have been captured. Being able to visualize and understand the drilling data allows for increased efficiencies, reducing the days on wells for operators from deepwater to inland barge and land drilling worldwide.
The development of the OLAP cubes required a multidisciplinary team consisting of software developers, business managers, domain champions, field-based engineers, and data scientists. The OLAP cubes consist of multidimensional databases built from relational and algorithmic interpretations of DRS transaction data. These algorithms are generated and developed by an iterative cycle of continuous improvement, development, and utilization of the OLAP cubes in parallel to improve the functionality and business impact for performance analysis, sales, product development, product reliability, and marketing. The data can be analyzed and visualized in the Microsoft Office suite by directly querying the DRS OLAP cubes. This also allows for dashboards to be updated in real time as data are added to DRS.
OLAP cubes have been developed to analyze the performance of drill bits, motors, reamers, rotary steerable tools, and many more downhole tools. The DRS cubes assist in identifying failure causes on bits to identify high-risk intervals to better target products and parameters to reduce costly nonproductive time. Fit-for-purpose OLAP cubes have been developed to understand drilling efficiencies and strategies in multibit versus single-bit sections using variable trip speeds and field performance. Traditional business reports were made more efficient and auto-updated and dashboards were built to identify major business trends to equip business managers.
This OLAP cube development has allowed for increased usage of the world's largest drilling record database and has made it easier to access and analyze the data. Ultimately, the techniques and development described in this paper help answer business questions to make better business decisions through data-driven analytics.
Case Study: Subsea Field Development Using Innovate Jumper Mounted Flow Access Technology and Standardized Hardware.
Authors: Donald Ian , McDonald Craig George , Hudson Adam Dudley Lawrence , Bryce Tom Gerard 
Published: April 2019

Abstract
This paper presents various case studies of projects in Gulf of Mexico and West Africa, which demonstrate how innovative jumper mounted flow access technology was successfully adopted to provide a differentiated field development strategy based on standard subsea hardware while still accommodating production enhancing technologies across multiple subsea field developments.
The paper also will explain how the technology supports fast track, cost effective subsea tiebacks, by promoting standard equipment and allowing concurrent hardware deliveries to deliver first oil faster.
The paper will share client considerations regarding standardization of the main subsea hardware elements, subsea system flexibility and the existing solutions for measurement and intervention packages, such as multiphase flow meters.
Project critical paths typically dictate that technologies such as flow meters must be available to synchronize with XT, manifold and jumper manufacture, testing and installation. Thereafter, any retrofit or rework requires significant operational downtime to recover and replace that element of the subsea system.
Integrating the retrievable flow access module (FAM) technology into subsea architecture within the jumper envelope addresses these concerns. Promoting standardized XT and manifolds on a field or regional basis, while the FAM technology provides the flexibility to address the individual well production requirements.
Further explanation will be provided as to how operators can use the technology to maximize ultimate recovery by accessing a suite of technologies throughout the life of field for both new greenfield and existing brownfield applications. Technologies such as, flow measurement and control, well and flowline hydraulic intervention and remediation, well production pumping and compression, pre-commissioning and secondary well control.
Finally, the paper considers the strategic benefits of adopting the flow access technology for subsea field development, promoting subsea system standardization, Capex, schedule and risk reduction compared to the more typical alternatives.
Beatrice Offshore Wind Project, Wind Turbine Generator Foundation Design
Authors: MacLeay Alan John , Hodgson Trevor 
Published: April 2019

Abstract

Objectives/Scope
The Beatrice Offshore Wind project comprises the development of 84 number 7MW turbines located in the Moray Firth in the North of Scotland. It is one of the most northerly and exposed sites globally and also the deepest site for fixed foundations for offshore wind. The paper describes the design challenges and how they were addressed. The solutions are likely to be of interest to anyone else developing a deep water offshore wind project, especially with variable soil conditions and a significant water depth range.

Methods, Procedures, Process
The Beatrice WTG foundations were completed as an EPCI project with close integration between the EPCI contractor, designer, fabrication and installation teams. Across the site water depths ranged from 35-68m, however, this range was reduced when a small number of outliers were discounted. The final range was 38-55m. There was also a significant variation in soil conditions across the site and this created significant challenges when attempting to standardise the design.
Global analysis of the selected structures under wind and wave conditions was performed using Sequential Coupled Analysis (SCA) with BHawC and ANSYS ASAS software packages. The analysis was performed by passing information between the turbine supplier and the substructure designer to produce coupled wind and wave loading on the integrated jacket, WTG and tower system.

Results, Observations, and Conclusions
The jackets are the largest ever designed and installed for offshore wind. The solution developed was a 4 legged pre-piled jacket. The design was split up in to 5 clusters to address the water depth range. Associated with this a pile stick up range of 2-6m was adopted. No scour protection was used.
The top half of the jacket, transition piece and much of the secondary steelwork was standardised across the site. The base dimension for the jacket and pile diameter was also standardised across the site to allow for re-use of a pre-piling template.
The final foundations were installed in 2018.

Novel/Additive Information
The grouted connection between the jacket and the pre-piles includes the first application offshore of Masterflow 9800 grout.
Control of early age cycling was a key consideration in design of the jacket to pile interface.
Piles were designed in accordance with the Imperial College Pile ‘ICP’ effective-stress pile design approaches for offshore foundations.
Elimination of False Positives in VIV Identification Using Machine Learning
Authors: Agarwal Puneet , Bhalla Kenneth , Campbell Robert Brad 
Published: April 2019

Vortex-induced-vibration (VIV) is an important consideration while drilling at sites with moderate to high current speeds. Planning for drilling operations often includes determination of limits on maximum drilling riser motion amplitude using model simulations. These limits can then be used to raise alarms in the field by comparing motions measured in the field using one or more motion sensors. The determination of such alarm limits is challenging as VIV is a highly nonlinear process, and small changes in the speed or shape of current profile can result in quite different VIV fatigue results for drilling risers, especially in deep water depths.
We use feed-forward neural network, which is a powerful machine learning algorithm, to develop a classifier for distinguishing damaging and non-damaging VIV events. The neural network uses acceleration and angular rate data from only three motions sensors located on the upper flex joint, the lower flex joint and the BOP stack. To train the neural network, riser motions and fatigue damage data are generated from SHEAR7 runs on the model of the drilling riser. Thousands of current profiles measured from a current mooring at a deepwater site (water depth &gt; 6,000 ft) are used as inputs to SHEAR7 model in order to capture full variability in VIV response from the actual field environment.
Results show that the neural network classifier almost always predicts damaging and non-damaging VIV correctly. The precision, recall, and F1 score (a combination of precision and recall) for the neural network classifier are all close to 100%. A high precision, recall, and F1 score for a classifier implies that it has no false positives and no false negatives. Here, a false negative is defined as the situation when damaging VIV occurred but was identified as a non-damaging VIV event and an alarm is not raised. False positive is the situation when an alarm is raised for damaging VIV when the event was actually not so damaging.
On the other hand, the baseline "constant" classifier of conservatively chosen limits (from the same data) for upper and lower flex joint angles results in very low precision and F1 scores, implying too many false positives. While the baseline classifier does not predict any false negatives, it is very expensive because of too many false positives. Furthermore, it carries the risk of being ignored by users due to too many false alarms.
This work demonstrates that machine learning techniques can accurately predict damaging VIV events in the field using minimal number of sensors. Such accurate predictions were not possible using traditional methods.
Reliability Study on the Life Extension of Subsea Umbilical Systems
Authors: Melo Joao , Silva Murilo 
Published: April 2019

Abstract
Subsea umbilicals are a critical element on most subsea oil & gas production systems. They can include hydraulic conduits and electrical and/or optical cables that serve as a critical lifeline, connecting topside facilities and equipment to subsea architecture, distributing multiple elements including power, chemicals, and communication functions throughout the deepest and harshest of environments.
Globally, thousands of subsea umbilical systems are approaching or have reached the end of their design lives after 15-25 years of operation. In many cases, operators would have a need to continue using such umbilical systems for longer, but they are unsure whether that can be done or if they need to invest in new replacement umbilicals. To make the right decision, operators need a well-defined process for evaluating the risks of continuing to produce using the existing umbilical systems.
This paper presents a simple methodology for assessing risks related to extending the life of subsea umbilical systems. This method helps operators identify the most critical areas of the system and their associated risks, and helps estimate the reliability of the system in its current and future states.
Upon review of available literature and current practices, it was determined that there were no industry standards for extending the life of umbilicals. The method described here is in line with the guidelines and recommendations of API RP 17N [Ref. 1]. The approach includes a review of the available information related to the life of the asset along with risk identification, risk assessment and recommendations for risk mitigation. Reliability models using field data and available literature are used to quantify the system reliability under different conditions, including life-extension scenarios. The proposed method is demonstrated using an actual business case from the North Sea, where risks associated with the life extension of a given umbilical system were identified and reviewed, and where the work has supported the operator's decision-making process.
Kaikias Phased Appraisal and Development Strategy
Authors: Hyder Scott C , Baird John I 
Published: April 2019

Abstract
Kaikias is an oil discovery of four stacked miocene reservoirs in the Deepwater Gulf of Mexico in the Greater Mars/Ursa Area. During exploration, only the lowest reservoir objective (Lambda) was penetrated, discovering oil pay to base. A follow-up appraisal well was drilled penetrating the upper three reservoir objectives (Beta, Zeta, and Kappa) discovering oil pay to base in all reservoirs, as well as penetrating the lowest reservoir objective (Lambda) and again discovering oil pay to base. Following the exploration and appraisal program, there remained considerable resource uncertainty given the lack of definitive oil water contacts in any of the reservoirs and poor seismic imaging (impacting the ability to determine the reservoir extent and thickness). Rather than continue to de-risk the field with further exploration and appraisal activities (potentially eroding lifecycle value and delaying first oil), a small, robust Phase 1 project was matured to accelerate first oil and to de-risk the subsurface through production data. The Phase 1 project was highly competitive attributable to the re-use of two existing exploration and appraisal wellbores and a minimal subsea scope. The Phase 1 project was sanctioned by Shell (80%) and MOEX NA (20%) in January 2017.
During execution of Phase 1, a Phase 2 project was proposed to further appraise the three reservoirs developed by Phase 1 (Beta, Kappa, and Lambda) as well as to produce unique volumes from the fourth reservoir (Zeta). The Phase 2 appraisal program was a great success, proving upside in all three Phase 1 reservoirs and justifying expansion of the two lowest reservoirs (Kappa and Lambda). Following the successful Phase 2 appraisal and subsequent side track to the Zeta reservoir, a Phase 3 project was proposed to add a second production well to each of the two lowest reservoirs, accelerating and capturing unique volumes from each. The Phase 3 project also provided an opportunity to calibrate seismic imaging for further exploration in the area and to provide a better understanding of fluid gradients. This phased appraisal and development approach resulted in a highly a competitive investment for Shell (80%) and MOEX NA (20%) and allowed co-owners to optimize the lifecycle value of the project without overspending on exploration and appraisal activities. Further, the phased appraisal and development approach reduced the overall downside risk for the project.