bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges in Drilling & Completion of Extended Reach Drilling Wells with Landing Point Departure More Than 10,000ft in Light/ Slim Casing Design.
Authors: Unnikrishnan Nitheesh Kumar , Al Ameri Saleh , Al Yamani Sultan Mohamed 
Published: November 2020

Abstract
Extended reach drilling (ERD) wells involves numerous drilling challenges that in many cases causes cost overrun and delays for such wells. This paper presents a new approach used to drill a 10,000 ft long departure using a slim design to replace the conventionally well design that uses heavy casings. This approach eliminated the 22" hole section and the associated 18 5/8" casing reducing 20 days form the well duration and saving $1 million.
Achieving such a long departure requires a shallower kick off and requires building inclination as early as possible crossing reactive shales and need high mud weight to guarantee hole stability. Historically an additional casing string used in order to isolate a loss zone prior to the shale formation to drill it with high mud weight. This approach limited the inclination across shales and penetrated it along with the loss zone in same section by controlling the WBM weight to be just high enough for bore stability and minimizing the loss rate but increasing the challenges in the next sections.
Drilling the single horizontal oil producer well started with a 16" hole size, kicked off at 300 ft and built to 22-degree inclination in that section and isolated with 13-3/8" surface casing. A 12-1/4" hole was drilled across the loss zone and reactive shales formation keep the WBM weight as 9.4 ppg (70 PCF) while drilling the loss zone and increased later to 9.9 ppg (74 pcf) prior to drilling the reactive shale where the inclination was limited to 27 degree across it and built later to 32 deg at the end of 12 ¼" hole section and isolated with 9-5/8" production casing.8 ½" hole section was drilled with 12.3 ppg (92 pcf) OBM in order to built 82 deg before penetrating the reactive shale and hold it tangent to cover the maximum departure and built to 90 deg to land in target reservoir with total departure of 10,755 ft and isolated with 8,150 ft long 7" production liner, followed by 4,000 ft 6‘‘ horizontal open hole. This novel light casing design avoided drilling a hole section of 22‘’ and the associated 18-5/8" casing, saving at least 20 days in well duration and nearly $1 million in well cost.
The new slim well design explained in this paper, which is used to drill long departure well with 10,000 ft, is the first of its kind in ADNOC Onshore field. It has proved that such long departure wells can be completed with light casing design and achieve 100% integrity.
A Machine-Learning Approach to Removing Highly Variable Surface-Waves for a Large Shallow-Water Survey Offshore Abu Dhabi
Authors: Badreia Al Hajeri , Guillaume Cambois , Mohamed Mahgoub , Mohamed Al Sarmi , Sonia Domont , Fritz Damianus 
Published: November 2020

Abstract
A large, full-azimuth, shallow-water ocean-bottom node (OBN) survey offshore Abu Dhabi revealed an intriguing azimuth-dependent linear noise pattern. The mud rolls and guided waves appear to curve along surface and near-surface features. Although we have yet to fully understand the mechanism that causes this phenomenon, we need to accurately and effectively eliminate these linear noises to meet the tight processing deadline. In view of the huge amount of data involved, we implemented a data-driven machine learning (ML) technology.
Mud rolls and guided waves are dispersive in nature. The standard way to remove them is to compute the dispersion curve (DC) (phase velocity as a function of frequency) from the data, model the corresponding mode, and subtract the model from the data in an adaptive way. In our case, the linear noise patterns are not only related to the azimuth angle of acquisition, but they also vary spatially, which makes removing them even more challenging. Since we cannot manually select the dispersion curves (DC) given the size of the data, we implemented an ML algorithm to classify the noise pattern and design the best noise removal operator for each category. The remaining outliers can then be resolved using customized solutions. In order to transform seismic data analysis into a pattern recognition problem, we need to generate attribute maps. In our case, one of the attribute map is the dispersion curve (DC), which has been calculated from the data. The maps are used as input to the ML stream which identifies classes by unsupervised K-means clustering analysis. Another attribute used is the root-mean- square (RMS) amplitude calculated over a fixed time window for each receiver gather. The corresponding maps are used as the input to the ML flow, which involves two-steps: first reduce the dimensionality of the input data by performing a principal component analysis (PCA), then identify classes through unsupervised K-mean clustering analysis. The generated class determines the common linear noise behavior and its spatial distribution group, thereby effectively driving the linear noise attenuation parameter setup.
First Piloting of Tight Oil Recovery in a Marginal Greenfield: Technology Selection and Planning Considerations for the First Multistage Acid Fracturing, Offshore AbuDhabi
Authors: Al Mazroui Sultan , Al Menhali Helal , Samantray Ajay , Al Shekaili Fatema , Al Hosani Fahad 
Published: November 2020

AbstractThe paper describes the planning and design of the first pilot well to unlock tight oil from a heterogeneous Jurassic carbonate reservoir, offshore Abu Dhabi. Reservoir permeability ranges from 0.1 to 5mD, with more than 80% of oil in-place locked up in the 0.1-0.3mD rocks. Conventional perforation, acid stimulation and zonal tests were seen to result in unsustainable flow and very low productivity index. Prior to committing capital investment, a decision was made to screen different completion and production technologies to exploit tight reservoirs, and deploy a pilot well to assess the feasibility of commercial recovery.The pilot study addressed the full value chain of upstream oil development, from static and dynamic modeling, drilling, completion and stimulation strategy, geomechanics and technology options screening including assessment of subsurface uncertainties, material requirements, and economics. Several well construction, stimulation and completion options were screened including stair-step horizontals, multilaterals, fishbone wells, multistage hydraulic fracking, stem tunneling and fishbone stimulation. Multistage acid frac was found to be technically and economically feasible offering improved recovery. Furthermore, this option is Abu Dhabi’s first ever offshore multi stage acid frac pilot, which is expected to lead and potentially unlock the rest of offshore tight reservoirs across the ADNOC Group.
Rock Drillability Intelligent Prediction for a Complex Lithology Using Artificial Neural Network
Authors: Gamal Hany , Elkatatny Salaheldin , Abdulraheem Abdulazeez 
Published: November 2020

AbstractThe fourth industrial revolution and its vision for developing and governing the technologies supported artificial intelligence (AI) applications in the different petroleum industry disciplines. Therefore, the objective of this paper is to use the artificial neural network (ANN) to build a model for the rate of penetration (ROP) that considers the effect of drilling parameters,formation lithology, and drill bit design on the ROP performance. The novelty in this study is addressing the influence of poly diamond crystalline (PDC) bit design as the number of blades and cutter size, bit nozzle total flowing area (TFA),and combined different drilled formations on the penetration rate. The well drilling data covered the 8-3/8" phase with more than 1000 readings for each input.The input data are the weight on bit (WOB),revolution per minute (RPM), torque (T), standpipe pressure (SPP),and mudflow rate (Q), mud weight (MWin), gamma-ray (GR), bit design codes as the number of blades and cutter size, bit nozzle, and total flowing area (TFA).The data training to testing ratio was 70: 30%. Another data set from the same filed was used to validate the model and the results showed high accuracy for the ANN-ROP model. The model provides a high performance and accuracy level with correlation coefficient (R) of 0.99, 0.98, and 0.98 and an average absolute percentage error (AAPE) of 4.36 %, 7.06 %, and 8.14 % for training, testing, and validating respectively.
Application of Stratigraphic Forward Modelling to Carbonate Reservoir Characterization – A New Paradigm from the Albion R&D Project
Authors: Borgomano Jean , Massonnat Gérard , Lanteaume Cyprien , Danquigny Charles , Samson Philippe , Rolando Jean-Paul , Rebelle Michel 
Published: November 2020

AbstractImproving carbonate reservoir prediction, field development and production forecasts, especially in zones lacking data, requires novel reservoir modelling approaches including process-based methods. Classical geostatistic modelling methods alone cannot match this challenge, in particular if subtle stratigraphic architectures or sedimentary and diagenetic geometries, not directly identified as properties with well data, control the reservoir heterogeneity. Stratigraphic forward modelling approaches can provide pertinent information to carbonate reservoir characterization. Integrating carbonate stratigraphic forward models in reservoir modelling workflows faces two sets of challenges: (1) the difficulty to establish high resolution and multi-scales continuous reservoir analogues from outcrops and (2) the development of forward modelling methods adapted to carbonate heterogeneity and calibrated to well data. The ALBION R&D Project dedicated to the Lower Cretaceous rudist-rich carbonate platform meets these two considerable scientific challenges. This workflow builds on previous modelling approaches at basin scale that integrates seismic inversion and forward modelling. This project delivers deterministic high-resolution static models that support the development of process-based modelling solutions. This multidisciplinary approach focuses on the characterization of primary sedimentary and diagenetic overprint that can impact drastically the flow behavior at inter-well scales in similar reservoir units such as the Kharaib and Shuaiba Formations. The SED-RES modelling package developed in house by Total R&D has been tested and calibrated with these high-resolution stratigraphic outcrop models. It allows valid prediction of carbonate facies associations mimicking the spatial distribution mapped along the Urgonian platform transects.
Deployment of Water Tracers Using Modified Dummy Gas Lift Valves – An Added Value to Field Operations and Reservoir Management
Authors: Gomes Jorge , Narayanan Ram , Alshmakhy Ahmed , Al Rawahi Ali 
Published: November 2020

Abstract
The objective of the paper is to share knowledge on an innovative design of the conventional dummy gas lift valves (GLV) which allows the deployment of water tracers within a polymer matrix (Figure-1). These tracers are released in contact with water and are detectable at surface as soon as water breaks through in the well. This technology does not compete with the established and more expensive conventional tracer deployment downhole, in segmented horizontal wells, for instance, whereby the reservoir zones producing water are detectable. Here, the deployment is in the upper completion of any well equipped with gas lift mandrels, whereby a new design of a dummy valve allows the inclusion of water tracers which are released in contact with water. At surface, in the first stage separator, or manifold, which receives fluids from many wells, a specific well producing water can be detected (Figure-2). Like any dummy GLV, this new tool containing tracers can also be deployed using a normal slick line and does not prevent any cased hole logging operation, like other downhole tracer deployment systems in the market.
Artificial Intelligence to Enhance Corrosion Under Insulation Inspection
Authors: Amer Ayman , Al-Shehri Ali , Cunningham Vincent , Saiari Hamad , Alshamrany Abdulaziz , Meshaikhis Ali 
Published: November 2020

Abstract
A critical challenge facing the integrity of many assets throughout the oil and gas industry is directly related to corrosion under insulation (CUI). Unfortunately, the lack of adequate inspection technologies adds to this well-known industrial challenge. Presented in this paper is an inspection tool enhanced using Artificial Intelligence (AI) that can provide field inspection engineers with a facility heat map of insulated asset integrity allowing inspection prioritization.
The approach used in this research, and presented here, was to enhance the output of already known and field approved thermographic technologies using a purpose built AI based on Machine Learning (ML). By examining the progression of thermal images, captured over time (&lt;20 minutes), corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. Additional benefits to this technique include enhanced safety through remote inspection and additional cost savings from monitoring assets online.
To develop and verify the CUI technology results from in-house laboratory tests followed by field validation outcomes will be presented. Laboratory trials were carried out using a series of insulated field assets with different levels of degradation and structural integrity set up to mimic the thermal behavior of in-process assets. This initial feasibility study allowed the definition of key parameters required to build an effective ML model. Following in-house trials a series of field tests and visual verification was performed on both hot and cold insulated assets to gather a sufficient amount of datasets to train the predictive algorithm. To enhance this learning process, synthetic data was created based on real field asset configurations and operating parameters. Finally, during the technology validation phase, again on field assets, the AI technique coupled with a commercial field approved thermographic camera returned a predictive accuracy in the range of 85 – 90%.
The work presented in this paper provides a solution for the current lack of technologies to monitor the presence of CUI by enabling and enhancing the output from already known and field approved technologies, such as thermography, using AI. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the complexity of asset preparation (scaffolding) and downtime.
Novel Methods for Production Data Forecast Utilizing Machine Learning and Dynamic Mode Decomposition
Authors: Gryzlov Anton , Safonov Sergey , Alkhalaf Muqbil , Arsalan Muhammad 
Published: November 2020

Abstract
Multiphase flowrate measurements play an important role during the reservoir characterization and production optimization phase of reservoir management. Accurate multiphase flow rate measurement is an indispensable tool for production optimization from oil and gas fields. One of the industry's accepted solutions is the use of multiphase flow meters, which are expensive, have a limited operational envelope, and are exposed to erosion and failures. This can limit the applicability of physical metering devices due to frequent calibration, transportation issues, space, safety, security, and possible high costs.
Virtual flow metering (VFM) is a method for estimating oil, gas and water flowrates produced from wells without measuring them directly. The method uses data available from the field, such as downhole pressure and temperature measurements as well as a choke position and ESP operational parameters, to estimate the flowrates by implementing hydrodynamic multiphase models, measurement data, and a reconciliation algorithm. In this paper, an overview of the conventional multiphase flow metering solutions is presented, which is followed by application of some advanced artificial intelligence and data analytics techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore.
The considered case refers to a typical scenario, where the measurements of oil, gas, and water flow rates are obtained in real time using a topside multiphase flow meter. Alternatively, the values of these multiphase rates are estimated using a data-driven dynamic flow model obtained using a dynamic mode decomposition technique. The results obtained with this method are compared with another VFM approach, where the rates are obtained using deep LSTM neural network.
Automated Tank Breathing
Authors: Sadid Metaab , Faraj Abdulla , Shateri Ahmed , Gopal Arun , Mahroof Dhasleem , Ahmed Niyas 
Published: November 2020

Abstract
Our Terminal Operation is fully automated wherein tanks and pumps are assigned by the system and human interface is marginal. In automated operation we need to segregate the tanks and pumps to avoid cross contamination. Blocked sections of pipe are exposed to thermal expansion and activation of Pressure Relief valves (RV). This resulted in the pipes exposed to pressure cycles and product being released by RV. System developed to address the issue.
Our Terminal operates on a start Stop mode. We identified the major Pipe sections which are responsible for the thermal expansion in the facility. These major Pipe sections were kept open to the same product tank at low level. This to ensure all thermal expansion in the pipe section are released to the tank. When the tank is designated for Breathing mode, then the tank is blocked by the system for any other operation to avoid the migration of undesired prodcucts to the tank. This is visually indicated in the screen to Alert the Operation Crew.
The entire terminal handled four major products. Pipe sections were grouped based on product type. Major Pipe sections were identified in each group. Frequency of the pipe section being locked were analyzed and a statistical model was evolved to identify the pipe section which required to be kept in breathing. Once the Pipe sections were identified then the possible destination for these pipe sections were identified. For each destination the path is identifiedto allow assets within this path to be recognized. The current state of the asset is ascertained, e.g. is it available for operation or subject tomaintenance. If the asset is subject to maintenance, then alternate path is explored. The path selection criteria are based on the minimum number of assets which should be in the selected path. Once the path is chosen the asset and the tank is blocked for breathing for that particular blocked pipe section.
In the event that the Pipe section, asset or Tank is required for Operation, then an alternate path is chosen for breathing. Any Pipe section that could be kept in breathing is alerted to the Terminal Operation crew, that at the earliest they need to keep the pipe section under breathing.
This proposed approach intends to replace previous practices where the thermal expansion of the hydrocarbon was released to the closed blow down system, which resulted in loss of finished product. The pipe section was exposed to cyclic pressure cycle, which increased the fatigue in the pipes and rendering the pipes operating close to their design pressure each time the relief valve was operated.
The simplicity and ease of operation is one of the key features offered by the proposed system. It also ensures the safety integrity of the Terminal assets.
An Innovative Carbonate Facies Modeling Workflow That Honors Geological Concept, Case Study of Thamama
Authors: Aidarbayev Sanat , Pamungkas Setiyo , Al Dayyani Taha , Lehmann Christoph , Ramos Luis 
Published: November 2020

Abstract

Objectives/Scope
One of the main uncertainties of reservoir modelling is understanding the reservoir properties in the inter-well region. Capturing variation in reservoir facies related to relative sea-level changes together with diagenetic overprint provides key information for the lateral reservoir heterogeneity. These observations can eventually be used as input for facies modelling to populate facies throughout the field. The conventional facies modelling approach defines lateral facies trends by using proportion data (pie-charts) for each reservoir zone. This approach has limitations to interpret lateral extension of individual facies or trends as the pie-charts show that all facies exist in the zone and everywhere throughout the field. However, incorporating the sequence stratigraphic concept into facies modelling workflow allowed us to draw discrete facies boundaries on sequence stratigraphic surfaces, which are of chronostratigraphic significance.

Methods, Procedures, Process
Integrated working sessions between sedimentologists and reservoir modellers lead to a new improved workflow in modelling reservoir facies. This case study provides a step-by-step workflow of the innovative facies modelling which is composed of three steps:
Create vertical proportion curve (VPC) to capture vertical 1D heterogeneity associated with higher order of sea-level cycles Construct pie-charts at important and critical sequence-stratigraphic surfaces, such as Sequence Boundaries and Maximum Flooding Surfaces. At this stage, a conceptual geological model is established that will serve to identify facies trends and heterogeneity in 2D. Defining probability maps to control the proportion of facies laterally to ensure facies ratios respect to each other.

Results, Observations, Conclusions
This workflow was applied to multiple stacked reservoirs of one of the UAE Onshore fields. Results started to show how the depositional environment controlled facies distribution over the field and helped to control reservoir properties of individual facies. With this workflow, the lateral facies predictability has increased. Sedimentological maps now help better to understand possible pathways for hydrocarbon flow from injector to producer wells. Incorporating higher resolution sequence stratigraphic cycles helped to understand the high-perm zones that are dynamically important to capture. The method was further tested in other multiple ADNOC Onshore fields where new geological facies maps also revealed reliable geological trends.