bluuebunny/crossref_metadata_2025 huggingface dataset:
Building The World’S First Offshore All-membrane Water Injection Plant
Authors: Orr Graeme , Rajkovic Tanja
Published: April 2020

Objectives/Scope

The objective of this paper is to describe the process technology and construction of the world's first seawater sulphate removal plant using membranes for the key treatment stages.

Methods, Procedures, Process

SUEZ and Techouse have jointly developed the water injection module for the Equinor FPSO Johan Castberg. The vessel will be stationed in the Barents Sea, north of the Arctic Circle in 360-390m of water. The water injection capacity is specified by the client as 180,000 barrels per day with a sulphate content of <40ppm and an oxygen content of <20ppb. The environmental challenges of this location meant that a fresh design perspective was required to operate the water injection plant to this specification. NORSOK engineering standards are also required.

The traditional treatment process for this type of installation is pre-filtration by multi-media, cartridge filtration, nanofiltration membranes for sulphate removal, followed by vacuum tower deoxygenation. This paper will describe the alternative process solutions proposed, using an all-membrane process train and describe the benefits in terms of weight, footprint and operability, plus progress with construction to date.

Results, Observations, Conclusions

The water injection module is structurally complete as at September 2019 and is unique in that it is fully enclosed. Construction has been optimised by using the walls as a structural component rather than just cladding, thus keeping weight and structural complexity to a minimum.

Novel/Additive Information

Unique features of this plant are:
Ocean Bottom Node Seismic and Full Waveform Inversion Towards Better Imaging Over a Complex Sub-Salt Field in Deepwater Gulf of Mexico
Authors: Mohapatra Gopal Krishna , Martins Wisley , Checkles Steve , Duncan Gregory
Published: April 2020

The Stampede Field is a sub-salt Miocene field in the deepwater Gulf of Mexico. Although it is a generally well imaged structure, development and production requirements warranted an improved image over the field. A variety of seismic data such as Narrow Azimuth (NAz), Wide Azimuth (WAz), and Dual Coil (i.e., Full Azimuth or FAz) have been used at various stages in the past. Imaging the field is complicated by the presence of up to 15,000 ft of salt which includes overhangs, inclusions, sutures, steeply dipping flanks, and a rugose base. Successful imaging of the northwest portion of the field has consistently been a challenge, regardless of the vintage or technique used in acquisition and processing.

Recently, Ocean Bottom Nodes (OBN) seismic acquisition technology has become popular due to its long offset and low frequency content, which are essential to address complex imaging issues. In 2018, Hess engaged Fairfield Nodal to acquire OBN seismic over the Stampede Field, which recently started production. Acquisition design was based on an OBN modeling study. Nodes were placed over 238 sq km with shots over an 888 sq km area. The survey was safely executed without any incident or lost time. The data recovery was excellent. Data was processed with technologies including Full Waveform Inversion (FWI) for velocity model building and Least Squares Reverse-Time Migration (LSRTM). Dual coil and WAz data were used along with OBN to produce a superior image of the field.

In this paper, we will discuss (1) the logistics of node acquisition over a producing field with infrastructure on the seabed and rigs at work and (2) the image uplift from processing the data using cutting edge technologies like FWI and LSRTM.
The Re-Use of Offshore Wellhead Platform to Facilitate A Marginal Field Development – Vestigo Experience
Authors: Jamil Mohd Rashid , Mustapha Munira , Othman Muammar Gadafi
Published: April 2020

This paper presents the successful delivery of Jitang Field Development project, a marginal field located offshore Peninsular Malaysia. Vestigo Petroleum Sdn Bhd (Vestigo), a wholly owned subsidiary of Petronas Carigali Sdn Bhd (PCSB) as operator of the field has set-up a new benchmark in Malaysia when Jitang field commenced production in January 2020:

The first in Malaysia to leverage on existing surface facilities via re-using/relocating Wellhead Platform (WHP)

Commenced production within 13 months from project's Final Investment Decision (FID)

Utilising suction pile technology and novel transport and installation (T&I) methodology to relocate the substructure and topsides of the WHP in a single piece from original to new location

Development of Jitang field consists of a WHP, an FPSO and a flowline evacuating full well stream from four wells. This paper will focus on WHP as the relocation of the WHP is the main feature and key driver of the project. Technical due diligence effort that was done in mitigating risks are also emphasized.

Vestigo's experience of re-using an existing WHP upon decommissioning the facility and re-installing it at a new location for re-use; proven that the concept works for effective solution of marginal field development. Jitang project has demonstrated significant improvement in project delivery schedule and cost saving.
A High-Resolution Regional Wave Resource Characterization for the U.S. West Coast
Authors: Wang Taiping , Yang Zhaoqing , Ticona Rollano Fadia Marion , Wu Wei-Cheng , García-Medina Gabriel
Published: April 2020

Objectives/Scope

Wave resource characterization is a critical step for wave energy converter deployment in the coastal ocean and relies on long-term, high-resolution wave datasets. This study presents a detailed modeling study of the wave resource along the U.S. West Coast (Washington, Oregon, and California), a coastal region that was identified with high wave energy potential in earlier studies.

Methods, Procedures, Process

The wave hindcast covers a 32-year period from 1979 to 2010 and is based on a multi-resolution, unstructured-grid SWAN model framework. Model configuration closely follows and meets the requirements recommended by the International Electrotechnical Commission Technical Specification (IEC TS) for wave energy resource assessment and characterization (Class 2 - feasibility study). The model domain covers the entire U.S. Exclusive Economic Zone (EEZ) in the West Coast and has a spatial resolution varying from ~300 m in the nearshore region (20 km from the shoreline) to ~2500 m within the EEZ and ~5000 m at the open boundary, which extends beyond the EEZ. The model was forced by hourly 2-D wave spectra produced by a two-way nested WaveWatch III model, which covers the global ocean domain and the broader U.S. West Coast region domain with spatial resolutions of 0.5 degree and 10 arc-minutes, respectively. Both wave models are forced by hourly, 0.5-degree wind forcing obtained from NCEP's Climate Forecast System Reanalysis (CFSR) product.

Results, Observations, Conclusions

The standard model output for the SWAN model includes 3-hourly output for the six IEC wave resource parameters (e.g., omnidirectional wave power) at each grid point and hourly 2-D spectra at more than 50 NDBC buoys. Extensive model validation was achieved by comparing the six model-predicted IEC parameters with those derived from field observations at representative NDBC buoys. The error statistics indicated the model's satisfactory performance. Further analyses were conducted to systematically evaluate the temporal and spatial distributions of wave energy potential and wave climate along the U.S. West Coast. Results suggest that Washington and Oregon coasts have similar nearshore wave resource, which is significantly higher than resources in Southern California. Strong seasonal variations are also observed, e.g., high wave energy tends to occur in the winter months. In summary, this study produced the first high-resolution, comprehensive dataset on wave energy distribution along the U.S. West Coast.

Novel/Additive Information

The results are being used by the National Renewable Energy Laboratory to update the MHK Atlas, which was originally derived from NOAA's 4-arc-minute WaveWatch III model output. In addition, the monthly averaged wave energy climatology dataset can be readily shared to support a variety of research and application efforts within the EEZ of the U.S. West Coast.
Subsea Leak Detection Systems at Appomattox
Authors: Thompson Stephen Taylor , Bentsen Stian , Manfield Philip David
Published: April 2020

The Appomattox asset, which currently has an expected production of 175,000 barrels of oil equivalent per day (boe/d), is a floating production system fed by several subsea production drill centers in the Gulf of Mexico, located in blocks MC437, 348, and 391-393. As the focus on in-situ leak detection has grown, two leak detection systems were implemented at Appomattox. Each uses an entirely different methodology for leak detection.

The Conditional Rate of Change (C-ROC) algorithm resides in the production control system and uses existing subsea pressure sensors. This algorithm was developed by Shell and is detailed in separate publications. Developed in parallel to the Appomattox project, C-ROC was added to the project later in the project lifecycle as it has become the Shell Gulf of Mexico standard for leak detection. In addition to the C-ROC method, each of the production drill centers has an Acoustic Leak Detector (ALD), provided by Naxys AS, a Baker Hughes business. These units are being piloted by Shell for the Gulf of Mexico. The ALD acoustically monitors the production manifold, wells, and associated flowline hardware at each drill center. The system is designed to distinguish the difference between background noise and actual leaks.

Implementation of each of these technologies leverage several other novel technologies and uses of existing technologies at Appomattox, including high bandwidth communications to support ALD and high-fidelity process simulation to both validate and train operations staff on the C-ROC algorithm.

This paper provides an overview of each leak detection system, details the technologies leveraged to implement these systems, and relates the project experiences thus far with each method.
Thermal Shock Transient Analysis for Well Integrity Through Operational Envelope
Authors: Agostini Cristiano Eduardo , Júnior Nelson Moreira , dos Santos Cleidilson Moura , Roman Roger Savoldi , Schnitzler Eduardo , Ferreira Marcus Vinicius Duarte , de Souza Charlton Okama , Weidlich Marcos Correa , Silva Alan Pinheiro
Published: April 2020

A physical evidence of an injector well failure caused by transient thermal behavior in cooling operations has lead to technical actions in order to provide a safe operational envelope. A modified thermal analysis was developed to predict the transient thermal behavior that can result in a cascade casing collapse. The as built approach was applied in a number of wells already constructed in a Brazilian offshore field resulting in a better way for selecting which mitigation method can be appropriate for each case. One of the main results from the study was to establish a controlled injection flow rate in order to avoid a cascade casing collapse due to thermal shock in the wellbore. In some cases, it was necessary to provide an annulus pressure communication in the wellhead using a ROV for bleeding off the excessive pressure in C annulus. With these actions, the wells studied were able to present a safe operational envelope. The approach commonly employed in the industry takes into account just steady state injection operations, which are not enough to describe what is happening in the field and also are not sufficient to support a suitable casing design. The industry must put efforts to develop and consolidate a methodology based on transient thermal analysis to deal with thermal shock events in the well, as proposed in this paper. Furthermore, this work also proposes four alternatives to mitigate the well integrity loss based on the transient thermal simulations and a comprehensive well risk assessement.
Increasing Facility Uptime Using Machine Learning and Physics-Based Hybrid Analytics in a Dynamic Digital Twin
Authors: Srikonda Rohit , Rastogi Ankur , Oestensen Haavard
Published: April 2020

The industry remains focused on achieving efficiency gains through accessing and processing production, asset and original equipment manufacturer (OEM) data, and applying machine learning (ML) principles to arrive at improved outcomes. As a service provider, we are experiencing an increased activity level related to hybrid analytics which involves embedding high-fidelity physics-based models together with ML models to improve outcomes. Critically, rather than addressing one piece of equipment, or a subset of a facility, companies focus on economies of scale, and seek asset-wide understanding of implications to equipment condition when changing operating parameters.

Deployment of a Dynamic Digital Twin provides production and maintenance engineers with a ‘single source of truth’ for information (i.e. P&ID, PFD, OEM information, maintenance history, etc.) integrated with high-fidelity physics-based models for subsea and topsides processes. Field sensors measuring hydrocarbon quantity, quality and other physical properties are integrated to provide real-time and historic data. Physics-based dynamic process models are first calibrated to match the field sensor data and then used to generate synthetic data for training ML models. A high-fidelity model generates virtual measurements where field sensors are not available. Access to such high-quality virtual measurements presents a paradigm shift for upstream analytics, as ML algorithms now have access to larger datasets for training. This improves quality, allowing for proactive planning and improved uptime leading to increased facility uptime by predicting equipment failure and enabling condition-based maintenance (CBM).

In our work with major oil and gas operators, we have observed that maintenance engineers until now have struggled, because enough field sensors are not always available to support the ML algorithms, leading to less specific assumptions and lower quality results. By taking advantage of a Dynamic Digital Twin - containing the asset structure, visualization and models - hybrid analytics were applied to continuously improve predictions, thereby increasing facility uptime.

In this paper, we present a few case studies of applying hybrid analytics with some oil and gas operators to enable virtual flow metering, prediction of unplanned equipment shutdown and prediction of optimum operating parameters for increased facility uptime. Examples presented demonstrate the integration of historic and real-time measurements with the physics-based process and multiphase flow models, and ML algorithms such as Autoencoder (AE), Long Short-term Memory (LSTM) neural networks and Reinforcement Learning.
Economic Feasibility Study of Several Utilization Alternatives For A Stranded Offshore Gas Reservoir
Authors: Trinh Khoi Viet , G. Moghanloo Rouzbeh Ghanbarnezhad
Published: April 2020

Floating Liquefied Natural Gas (FLNG) has been on the rise in recent years to meet growing energy demand, worldwide. As energy consumption and exploitation of onshore unconventional gas reservoirs continue to grow while gas price remains almost steady, FLNG can potentially become the key for operators in offshore gas fields through integration of upstream and midstream processes on the spot.

This paper compares project economics of a FLNG utilization to those of onshore LNG plant, and Gas-to-Wire (GTW) processes. Sensitivity analysis and tornado charts are used to evaluate importance of various uncertain parameters associated with FLNG construction and operation. Costs for the hypothetical FLNG vessel is taken from the average cost of Shell's Prelude FLNG; while pipeline, LNG plant, and gas-to-wire costs were obtained from typical industry standards. A typical hyperbolic decline curve model is applied to model depletion flow regime of production life after two scenarios: a 5- and 10-years constant rate plateau time, respectively.

Several factors are included in the sensitivity analysis: LNG price, interest rate, initial production rate, and condensate-to-gas ratio (CGR), distance from shore, electricity price, natural gas price and percentage share of overnight capital cost of building a power plant to convert gas to electricity. The factors are used to gauge their effects on the net present value (NPV) of each scenario and are ranked based on their sensitivity on a tornado chart, with the most sensitive parameter on top, and so on. The analysis suggest that initial production rate has the strongest effect on NPV, followed by discount rate, LNG price, CGR, and the distance from onshore when the reservoir is dry gas. This means the longer the distance from onshore, the more attractive the FLNG alternative becomes. However, when gas price is low, and a subsidy can be obtained, GTW becomes more attractive.

This economic feasibility study will be helpful for future considerations to use FLNG to make offshore gas reservoirs that were previously considered stranded into economically viable resources. The results from this economic model will certainly play a key role in the future of natural gas industry and energy market, especially in West Africa, particularly, in Nigeria.
Overview of Joint Industry Project on Measurement Based Wellhead Fatigue
Authors: Mercan Bulent , Chandra Yenny , Djayaputra Ferdinand , Campbell Mike , Kebadze Buba , Ge Michael , Tognarelli Mike , Lu Wangming , Adams Adrian , Kuzmichev Sergey , Jin Joe , Campbell Brad , Pattillo II P. David , Roveri Francisco , Pestana Rafael , Chen Jinbo , Zhang Heping , Zhang Michael , Fett Darryl , Sanders Richard
Published: April 2020

The objective of the wellhead fatigue joint industry project (JIP) is to provide a measurement-based foundation for drilling riser and wellhead modeling practice in the oil and gas industry and hence to ensure that the fatigue response assessment is performed with adequate but not overly conservative analysis parameters. To this end, the JIP utilizes field measurements from ten (10) drilling campaigns in GoM and North Sea. In order to maintain drilling campaign diversity, the field measurements are selected for a range of environments, water depths (110 to 1,900 m), soil characteristics, riser and wellhead configurations, and vessel types.

The study commences with field data QA and filtration. Measurements are classified into wave-dominated events, VIV events and combined wave and VIV events. The finite element models of the as-built riser and wellhead systems are generated using industry standard analysis parameters, and simulations are conducted using measured motions near the top of the riser. The resulting numerical responses for the wellhead are compared with the measured motions to determine the level of conservatism (or otherwise) in the wave fatigue analysis. Additionally, SHEAR7 models driven by measured current profiles are used to compare predicted VIV fatigue response to that based on field measurements.

Analysis results indicate that industry standard approach for wave and VIV fatigue assessment is indeed conservative. However, it should be noted that wellhead fatigue predictions through numerical simulations are affected by various analysis parameters, and it is impossible to determine the correct values for each of these parameters by using field measurements alone.

In literature, several previous studies compared measured and predicted wellhead response. However, they often focus on a single drilling campaign, which makes it difficult to apply their findings to another drilling campaign. This JIP is the first in the industry to provide a combined assessment of full-scale field data from multiple drilling campaigns. Using consistent analysis techniques for all datasets offers valuable insight into riser and wellhead response characterization and safe drilling operations.
The Emergence of Contact Based Nondestructive Testing NDT at Height Utilizing Aerial Robotic Drone Systems
Authors: Dahlstrom Robert L.
Published: April 2020

Corrosion engineers take Nondestructive Testing (NDT) measurements at height as a method of determining the condition of Oil & Gas and other assets such as flare stacks, boilers, above ground storage and pressure vessels, piping and more. To do this at elevation currently the engineer may need to utilize a lift, scaffolding, ladders, or ropework or even inspection trucks with elevated baskets, sometimes cranes or even specialty access such as rigging catwalks, or other solutions. The engineer will often hold a handheld digital testing device to the asset to take the measurements in these scenarios. While NDT inspection programs can dramatically increase the safety and integrity of infrastructure, industrial and manufacturing assets, access requirements in performing these inspections at elevation introduces risk. NDT inspection sites often require access to elevated areas and require the use of personal fall protection equipment (safety harness and lanyard). Working at height is dangerous, due to the possibility of falls, as well as being time-consuming due to access. In certain instances, it may also require taking an asset, such as a flare stack or chimney stack, offline to allow it to cool so it can be accessed to take NDT readings.

Utilizing an aerial robotics platform that makes contact with a surface in order to gather NDT measurements such as Dry Film Thickness (DFT), Surface Profile (SP), or Ultrasonic Thickness (UT) allows workers to remain safely on the ground. Since there is no need to move a lift, scaffolding or ladders and minimal movements required by the corrosion engineer or the robotic operator, the NDT measurement process can be faster in addition to being safer. Further, since the aerial robotic system (drone) contains onboard computers and sensors it is able to capture a lot of data during the exercise of an NDT inspection regime.

Use of these aerial robotic NDT systems in offshore and marine environments carries unique challenges and operational variables. The Aerial Robotic System described in this paper afford safer, cheaper, and better NDT measurements and allow a more robust viewpoint into assets conditions than the slower and more expensive manual method. While early adopters in the offshore Oil & Gas and maritime industry stand to gain the most from implementing these systems they are also assuming the most risk until the systems become an industrial standard.
Digitalization Deployed: Lessons Learned from Early Adopters
Authors: Nixon John
Published: April 2020

Offshore oil and gas operators may have been slow to realize the potential of the Internet of Things (IoT) and digital transformation, but with the market downturn slowly fading into the rearview mirror, the industry is quickly catching up. Still, there is much that companies can learn from the "early adopters" of digitalization. This paper discusses how digital technologies are being applied in other verticals that are further along the maturity curve (i.e., automotive, manufacturing, marine, aerospace) and how they can be leveraged to optimize the lifecycle performance of offshore oil and gas facilities.

The paper details the benefits of extending digitalization across the energy enterprise to drive down costs and decouple market volatility from profitability. Case studies will be shared from the aforementioned verticals to illustrate how advanced tools, such as digital twins and enterprise product lifecycle management (PLM) solutions have been successfully applied to reduce product cycle times and improve project execution. All of these are highly applicable to upstream oil and gas, and can serve as powerful tools for shortening time to first oil. Overall, the paper aims to provide a broader context for exploration and production (E&P) companies’ digital enterprise strategies.
Real-Time Physical Models with Learning Feedback as a Digital Twin Architecture
Authors: Dannenhauer Cristiano Edio , Bastos Baptista Gustavo Luiz , Szwarcman Dilza , Martins da Silva Ramises , Martins Plucenio Daniel
Published: April 2020

The use of numeric simulation tools has been a traditional approach for determining the behavior of equipment and complex engineering operations. These same techniques have been used in the oil & gas industry over the years to help improve efficiency and reduce cost, allowing the exploration of areas that were previously economically unfeasible. Currently, with the increasing development of sensors and communication technologies, real-time numerical simulation is becoming a key tool for a better understanding of drilling dynamics, allowing the prediction of critical events and making operations more efficient and secure. The goal of this work is to present a computational tool that applies the concept of digital-twin to oil drilling systems by proposing the use of physical models to simulate, in real-time, phenomena such as well hydraulics, torque and drag, cuttings concentrations and bed formation. Data collected in real-time from the rig is used as input to the physical models, whose outputs can flow into other models, allowing a multi-physics analysis. The data collected from the sensors and the results provided by the digital-twin tool are stored in a time series database, which is used for the continuous evaluation of errors and efficiency improvement, implementing a learning feedback into the physical model calculations of drilling operations. Enhancements on the accuracy of model calculations are shown, leveraging the aforementioned architecture to monitor well operations, such as hydraulic, torque and drag and cuttings transport, and by the validation of the results by subject matter experts. This work presents how the proposed approach incrementally improves closing equations coefficients with learning feedback, i.e. having historical data from sensors and calculated data continuously being stored into the system, using big data technologies for handling the large volume data.
Intelligent Pressure Control System on Drilling Process
Authors: Zhu John , Zeng Liam
Published: April 2020

MPD (managed pressure drilling) is an important technique for challenging drilling operations especially in narrow operational windows, the IPC (Intelligent Pressure Control) system is equipped with the ultra-compact MPD manifold (L3.6m*W2.3m*H2.8m) with complete functionality of measurement & automatic control system, benefit the operators on footprint reducing for limited field space especially offshore operations. The 3D interface of IPC software provides operator with a perceptual intuition about current status of the equipment.

IPC system doesn’t rely on PWD sensor to acquire the downhole pressure data, with the unique algorithm integrated in iPWD module, the real time downhole pressure data could be generated without any downhole PWD sensor, the deviation between iPWD data and real PWD data is within 3%, which was proven in over 40 wells operation.

Nebula system is an add-on feature for IPC system, using cloud and IoT (Internet of Things) technologies, the Nebula could track the equipment’s specific location, working status and drilling parameter, providing statistical diagnosis based on data collected from field operations, which helps operators to make decisions quickly. The data uploaded to cloud could generate different reports according to end user’s requirements, to analyze drilling operation challenges or difficulties, this could be an intelligent tool which helps operators learn and understand the features of different blocks smarter via big data analysis. You can receive all data provided by Nebula system on your cellphone and PC at any time anywhere once connected to cloud station, always keep an eye on field operation and get the latest drilling parameters.
Flow Assurance Risks Impact on Kuwait Offshore Field Development Plans
Authors: Al Safran Eissa M. , Al Ansari Sara I.
Published: April 2020

Kuwait Oil Company (KOC) offshore exploration and development plans are underway to boost its production capacity to the future target rate. However, the selection of offshore field development scheme is critical due to the associated flow assurance risks, which impact project economics, safety, and sustainability. The objective of this study is to simulate and evaluate two offshore field development schemes, namely subsea multiphase flow, and platform schemes. The evaluation is based on the associated flow assurance risks, project economics, and environmental impact of each development schemes. The analysis covers simulation of each scheme, prediction of flow assurance risks, and prevention/mitigation of the risks during the entire life of the field. Results revealed that although the flow assurance risks in the subsea multiphase flow scheme are significant, it eliminates the large investment cost of platform, which improves the project economics significantly, and minimize environmental impact. Conversely, although the platform development scheme has low flow assurance risk, platform capital investment cost may impair the project economics, safety, and environmental impact. This study investigates each scheme by simulating, economically evaluating, and environmental impact assessing both schemes, to enable the right decision and selection of field development plan.
Data Conditioning and Forecasting Methodology using Machine Learning on Production Data for a Well Pad
Authors: Bagheri Maryam , Zhao Haoran , Sun Manyang , Huang Li , Madasu Srinath , Lindner Peggy , Toti Giulia
Published: April 2020

A new machine learning (ML)/statistical-based methodology for conditioning and predicting production data for a well pad has been developed. Typically, data conditioning involves outlier detection, missing data, data imputation, and smoothing. Time-series production data prediction can be challenging because the target (wellbore oil production) depends on large-scale, high-dimensional data sets with unknown distributions and is influenced by missing data and outliers. Hence, data conditioning is key for accurate predictions. The current work is the first attempt at using ensemble ML and statistical techniques, such as multilayer perceptron (MLP), principal component analysis (PCA), and support vector regression (SVR), for well pad data conditioning using recently disclosed subsurface and production data from a field in the southern area of the Norwegian North Sea. The time-series forecasting based on large-scale, high-dimensional conditioned and cleaned data sets is also presented.

The data with an oil production rate greater than 10 Sm3 have been retained for data cleansing, which reduced the size of the production well data set by 14.9%. Outliers are detected using the z-score method. The missing values are predicted using a trained ML model on all available nonmissing data. The procedure first predicts the downhole missing values from all the wells, including the available neighboring wells, and then uses these features to predict other missing values for the well pad. In this paper, the two approaches implemented and compared for prediction of missing data are MLP and SVR, and PCA is performed to extract the most important data features.

Production data with 12 related variables (i.e., dates, hours, temperature, pressure, etc.) are used to explore the complex nonlinearity of features and estimate wellbore oil production with ML and deep-learning models. Conventional SVR and MLP methods are implemented as the benchmark.

During this work, more than 60% of the missing and abnormal data from the field data set are detected and imputed using advanced ML methods, such as MLP and SVR with radial basis function kernel. More than 6% of data are outliers and are removed using the z-score method. The modified SVR with time-series data structure and long short-term memory (LSTM) algorithms are used for the comparisons. An R-squared (R2) of 98% is achieved for both the algorithms; however, LSTM has the lowest root mean square error (RMSE) results compared to SVR.

Data conditioning is conventionally performed using statistical techniques, but here, an ensemble of ML techniques is used depending on the available data. This paper presents a new methodology to perform data conditioning and production prediction for a well pad using ML and neighboring well data. The ML algorithms used are highly efficient, as demonstrated by the results.
Qualification Of 20k Choke & Kill Unbonded Flexible Pipes For Drilling Applications
Authors: Bernard Gary , Peronne Simon , Vera Damien , Leborgne Flavien
Published: April 2020

Objectives/Scope

For thirty-five years, TechnipFMC has been providing high pressure and high temperature flexible pipes to the drilling industry. These unbonded flexible pipes rated up to 15k psi working pressure are used worldwide as part of the choke and kill system of onshore and offshore drilling facilities or units. Following recent high-pressure reserve discoveries and to enable future exploration of formations with anticipated higher pressures, the drilling industry has started upgrading drilling safety equipment like blowout preventers to higher pressure ratings. Unbonded flexible pipe technology is following this trend, striving to continue providing the industry with reliable high-pressure choke and kill flexible pipes.

Methods, Procedures, Process

The information presented in this document is the outcome of a full-scale test campaign performed within company owned facilities or in collaboration with worldwide recognized test laboratories.

Results, Observations, Conclusions

This paper will present the trials performed on the presently available 15k psi rated product and the results of the testing toward qualification of the first choke and kill unbonded flexible pipes with a 20k psi internal pressure rating. In addition to the high-pressure upgrade, progress was made in the fire performance domain by improving flexible pipe product knowledge with regard to the fire resistance. This has been achieved through a large fire test campaign performed over the years. The document will detail the tests performed, the procedures followed, and the understanding of the different failure modes observed.

Novel/Additive Information

Major achievements in the development of high pressure choke and kill unbonded flexible pipe technology will be presented to the industry with a focus on qualification testing and fire performance.
Estimation of Soil Parameters Based on Analytical Methods and Neural Networks for Conductor Casing Design Using CPTu Data
Authors: Barboza Beatriz Ramos , Várady Filho Christiano Augusto Ferrario , Esteves Aline Viana , Melo Jennifer Mikaella Ferreira , Tenorio Joyce Kelly Franca , Santos João Paulo Lima , Lima Junior Eduardo Toledo , Dias Rafael , Cutrim Fábio Sawada
Published: April 2020

Piezocone Penetration Test (CPTu) is widely used in offshore projects to obtain soil parameters, such as the undrained shear strength. Due to depth limitations to perform this test, it is common to obtain data until around 40m when the conductor installation process would require at least double the depth. The present work uses extrapolation techniques based on analytic and heuristic approaches to estimate data beyond the depth domain of CPTu tests. Design of the conductor casing is highly dependent on soil properties, since it serves as a foundation element of the well. Estimation of the soil parameters is based on deepwater CPTu data from Brazilian offshore basins. Three analytical approaches are used in this study (linear and non-linear regressions: second and third degrees). Moreover, Artificial Neural Networks (ANN) (dense, convolutional and recurrent networks) are also employed to predict the soil behavior. Methodologies were applied, validated and compared to evaluate their capability to accurately estimate the undrained shear strength. Python subroutines were developed and applied to sets of homogeneous and heterogeneous data from CPTu tests. The undrained shear strength was estimated beyond the test domain until the depth of interest to the conductor casing design around 80m. For this purpose, both groups of techniques were validated analyzing the efficiency of the fitting process, the associated error and coefficient of determination of each methodology. From that point on, we compared data from analytical methods and the neural networks application, verifying which technique fits better on the datasets. These methods of estimation of soil properties work as an instrument to support the decision-making process in top-hole drilling operations. The datasets analyzed present different levels of soil heterogeneity and performing the extrapolation analysis brings additive information to understand the soil behavior beyond depths reached by CPTu tests. This contributes to the safety and reliability of conductor casing design and installation. To the authors' knowledge, this analysis is rarely performed in the literature.
Critical Offshore Field Development Projects –Why Work Outside of Strict Timelines?
Authors: Arukhe James Ohioma , Al-Ghamdi Saleh Abdullah , Al-Nafisah Musa Mohammed
Published: April 2020

Overcoming environmental challenges to develop offshore oil fields remains daunting for engineers who must balance economic feasibility with environmental protection, often within limited timelines before first oil commitments. A critical examination of Saudi Arabia's field development cases reveals a 56-year timeline (which seems interminable) between offshore field discovery in 1957 and realization of an environmentally friendly field development in 2013. If teams desire to accomplish Aegian projects with the sustenance of core values in perspective (whatever the core values might be), such as to gain the economic advantages of offshore projects while preserving the environment or honoring some other constraint, it is imperative to work with more robust timelines. Whereas strictly time-bounding projects may help with releasing adrenalin for performance, engineers may require adequate time for innovative concept designs to conduct necessary pilots and qualification.

Collaboration between cross-functional project teams – construction, drilling, testing, and production – is examined from the economic delivery of several challenging wells under various environmental challenges and constraints. An overarching theme from each team was to apply environmental solutions. Colonization of hard coral species and presence of several fish species were observed on project completion. For example, a 70% increase in growth of Seagrass and artificial coral reefs was observed near the causeway built for the Manifa Field, which could become a hot spot of biodiversity. Water circulation, as measured by an acoustic doppler current profiler (ADCP) improved from 17 days before the project to 11 days on project completion. When engineers persevere, collaborate and have enough time to plan intensively until they find the technology solutions to advance, society benefits from an environmental return.

As the worldwide study cases reveal, several projects are threatened globally because engineers may be unable to find viable solutions under rigid timelines. Often companies must be willing to wait until they acquire the technologies to responsibly advance complex environmentally sensitive projects. If timelines are flexible and sufficiently realistic, management's unwavering support to avail necessary resources would be critical for success.
Advanced Supervised Machine Learning Algorithms for Efficient Electrofacies Classification of a Carbonate Reservoir in a Giant Southern Iraqi Oil Field
Authors: Al-Mudhafar Watheq J
Published: April 2020

Understanding the vertical discrete electrofacies distributions in wells is a vital step to preserve the reservoir heterogeneity. Predicting the electrofacies distribution at all wells is commonly conducted manually or with the use of some graphing approaches, but recently different machine learning techniques have been adopted to categorize electrofacies. In this paper, two supervised machine-learning techniques were implemented to model electrofacies given well logging data for a well in order to predict the distributions in all other wells (classification) in a carbonate reservoir in a giant southern Iraqi Oil Field.

The available data included open-hole and CPI well logging records in addition to the routine core analysis. The well discrete electrofacies distribution for the entire reservoir thickness has been obtained in our paper [OTC-29269-MS] using the Ward Hierarchical Clustering Analysis. For electrofacies classification, two supervised machine-learning techniques, K-Nearest Neighbors (KNN) and Random Forests (RF), were adopted to model the resulting electrofacies given the CPI well logging data for a well to predict at other wells that have missing data. These two supervised learning techniques were implemented as non-linear and non-parametric classifiers, which are imperative attribute due to the non-linearity of the electrofacies properties and the geological reservoir control.

The results of this research illustrated that the reservoir electrofacies can be predicted through the use of the supervised learning techniques when well logging records and core data are available. The two adopted classification algorithms were analyzed and compared based on confusion table, transition probability matrix and total percent correct (TCP) of the identified electrofacies that reveal the accuracy of the classification. RF was observed to be the optimum approach as it led to better electrofacies classification in this carbonate reservoir than the KNN.

The application of supervised machine learning techniques enhanced the accuracy and reduced the time spent in electrofacies classification. The two machine learning algorithms were implemented by R software, the most powerful statistical programming language.
Aquatic food web research in mesocosms: a literature survey
Authors: Póda Csenge , Jordán Ferenc
Published: April 2020

Food web research feeds ecology with elementary theoretical concepts that need controlled experimental testing. Mesocosm facilities offer multiple ways to execute experimental food web research in a rigorous way. We performed a literature survey to overview food web research implementing the mesocosm approach. Our goal was to summarise quantitatively how the mesocosm approach has formerly been used and question how to best utilise mesocosms for the emerging topics in food web research in the future. We suggest increasing the number of replicates, extending the duration of the experiments, involving higher trophic levels and addressing the combined effects of multiple stressors.
EPAC2: A new and promising protein for glioma pathogenesis and therapy
Authors: A. Richard Seidu
Published: April 2020

Gliomas are prime brain cancers which are initiated by malignant modification of neural stem cells, progenitor cells and differentiated glial cells such as astrocyte, oligodendrocyte as well as ependymal cells. Exchange proteins directly activated by cAMP (EPACs) are crucial cyclic adenosine 3’,5’-monophosphate (cAMP)-determined signaling pathways. Cyclic AMP-intermediated signaling events were utilized to transduce protein kinase A (PKA) leading to the detection of EPACs or cAMP-guanine exchange factors (cAMP-GEFs). EPACs have been detected as crucial proteins associated with the pathogenesis of neurological disorders as well as numerous human diseases. EPAC proteins have two isoforms. These isoforms are EPAC1 and EPAC2. EPAC2 also known as Rap guanine nucleotide exchange factor 4 (RAPGEF4) is generally expression in all neurites. Higher EAPC2 levels was detected in the cortex, hippocampus as well as striatum of adult mouse brain. Activation as well as over-secretion of EPAC2 triggers apoptosis in neurons and EPAC-triggered apoptosis was intermediated via the modulation of Bcl-2 interacting member protein (BIM). EPAC2 secretory levels has proven to be more in low-grade clinical glioma than high-grade clinical glioma. This review therefore explores the effects of EPAC2/RAPGEF4 on the pathogenesis of glioma instead of EPAC1 because EPAC2 and not EPAC1 is predominately expressed in the brain. Therefore, EPAC2 is most likely to modulate glioma pathogenesis rather than EPAC1.
Changing pattern of adult external abdominal hernias in Zaria
Authors: Abur Peter Pase , Daniyan Muhammad , Nwabuoku Stanley Emeka , Yusufu Lazarus M. D. , Odigie Vincent I.
Published: April 2020

Background:

External abdominal hernias are very common diseases encountered in surgical practice. A previous report from this centre 21 years ago documented the pattern of adult external abdominal hernias. However, there is an observed changing pattern. The aim of the study was to document the changing pattern, mode of presentation, treatment and outcome.

Methods:

It was a 5-year prospective study from January 2011 to December 2015. Adult patients with external abdominal hernia at our institution were studied. Information documented included patients' sociodemographic information, type of hernia, mode of presentation, treatment and outcome.

Results:

Six hundred and thirty-seven out of 4,083 patients with general surgical cases had external abdominal hernias (15.6%), with a male:female ratio of 3.1:1. The types of hernia were inguinal (451 [70.8%]), umbilical (83 [13.0%]), incisional (54 [8.5%]), epigastric (31 [4.9%]), femoral (14 [2.2%]) and others (4 [0.6%]). The common modes of presentation for inguinal hernias were simple (364 [80.7%]) and strangulated (42 [9.3%]). The most common mode of treatment for inguinal hernias was modified Bassini (265 [58.8%]). The common post-operative morbidities for groin hernias were wound infection (18 [3.9%]) and acute urinary retention (10 [2.2%]). The 3-year recurrence rate for groin hernias was 14 (3.0%). Mortality was three (0.5%) patients.

Conclusion:

The pattern of external abdominal hernias in our institution has changed with the descending order of occurrence as follows: inguinal, umbilical, incisional, epigastric and femoral. This is in contrast to previous reports where femoral was the second most common. Modified Bassini was the preferred method of repair of inguinal hernia due to its simplicity.
Algorithm to detect and calculate the cerebrospinal fluid (CSF) through Magnetic Resonance Imaging (MRI)
Authors: González-Martín Enrique , Sosa-Gómez Guillermo , Rojas Omar
Published: April 2020

INTRODUCTION: An heuristic algorithm for detection and segmentation of cerebrospinal ﬂuid through magnetic resonance imaging is presented.OBJECTIVE: The objective of the method is to calculate the minimum amount of anesthesia to be used in patients at the time of an operation and thus make a decision based on the algorithm.METHODS: The pattern-based CSF segmentation algorithm presented is based on ﬁnite cellular automata that takes as input light and dark images obtained from MRI methods.RESULTS: Based on Fieldman’s test, results show that the algorithm is as eﬃcient as the decision taken by two diﬀerent operators regarding the mL of anesthesia to be used in the patient.
Quantum groups with projection and extensions of locally compact quantum groups
Authors: Kasprzak Paweł , Sołtan Piotr M.
Published: April 2020

The main result of the paper is the characterization of those locally compact quantum groups with projection, i.e. non-commutative analogs of semidirect products, which are extensions as defined by L. Vainerman and S. Vaes. It turns out that quantum groups with projection are usually not extensions.We discuss several examples including the quantum

\mathrm{U}_q(2)

. The major tool used to obtain these results is the co-duality for coideals in algebras of functions on locally compact quantum groups and the concept of a normal coideal.