bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drilling Offshore Wells with HP WBM in Extreme HP HT Conditions
Authors: Affede Luca , Lorefice Roberto , Pinto Vieira Larissa , Giubertoni Matteo , Buzzi Lorenzo , Carpineta Gabriele 
Published: February 2022

Abstract
During drilling of three exploration wells challenging conditions encountered, such as temperatures up to 180°C, interbedded highly reactive shales/silts, formation pressures which required mud weights up to 2.35 sg and narrow margin between pore and fracture gradients, posed a host of technical, logistical and cost challenges to Eni activities. These conditions required an accurate drilling fluids design to maximize operational efficiency and to minimize the risks related to such an extreme environment.
Technical demands were particularly critical since the reactive shale formations had historically proved to be difficult to inhibit when drilled with Water Based Mud and might have caused swelling, tight hole, sticky wireline runs, bit-balling and accretion that could have resulted, among other issues, in low penetration rates (ROP). The formation nature coupled with ECD (Equivalent Circulation Density) constraints due to the high mud weight required to cope with high pore pressure, which also caused high mud rheology readings, were therefore the main limits to be overcome to achieve the well objectives.
A tailored drilling fluid program was thus proposed which consisted of an inhibitive HPWBM (High Performance Water Based Mud) that could be converted to an HT-HPWBM, (High Temperature-High Performances Water Based Mud) while drilling, to cross the deeper and hotter sections of the well. This fluid was specifically engineered and optimized after each well in order to contain high concentration of a combination of monovalent salts to guarantee inhibition and reduce solids loading, dedicated polyamine shale inhibitor and fluid loss additives to minimize API/HPHT filtrate and filter cake thickness with the aim to reduce shale water invasion throughout the drilling campaign, graphite to minimizes fluid invasion and fracture propagation and ROP (Rate Of Penetration) enhancer continuously injected using dedicated pump to act as anti-balling and anti-accretion additive.
The achieved results were drilling targets delivered safely, on time and with good overall fluid performances which either reduced or eliminated many of the challenges seen in offset wells, including: no barite sag, rheology stability, and stable long-term mud properties and wellbore conditions even during extended formation logs acquisitions.
This paper covers the design, execution and accomplishments of the water-based drilling fluids employed on three HP/HT wells drilled, together with all of the lessons learned captured, highlighting the evolution of these systems to reach a step-change in terms of performances in such a harsh environment.
Autonomous Viscosity/Density Sensing System for Drilling Edge-Computing System
Authors: Gonzalez Miguel , Adams Robert , Thiel Tim , Gooneratne Chinthaka , Magana-Mora Arturo , Safran Ali , Ghamdi Faisal , Powell Chris , Hulse Ed , Ramasamy Jothibasu , Deffenbaugh Max 
Published: February 2022

Abstract
Current mud monitoring practices are limited due to their reliance on manual measurements such as funnel viscometers, weight balances, or basic field rheometers. These manual practices impose restraints on the quantity and quality of the available data that are essential to ensure optimal and safe drilling operations. In this study, we introduce a new autonomous mud viscosity/density system based on an electromechanical tuning fork resonator. The system was integrated into an edge-computing system for improved data collection and deployment of machine learning models. The system was tested during a live drilling campaign. The viscosity/density sensor is based on an electromechanical tuning fork resonator. The sensor was integrated into a submergible housing for in-tank measurements. Two systems were developed for simultaneous measurements at inflow (possum belly) and outflow (suction pit). The data from the two systems were broadcast wirelessly to the central computer room at the rig for real-time display and data aggregation by the edge-computing system for the development of time-series analysis models using machine learning. During initial field testing, data from a single sensor were collected for various hours at a rate less than a sample per second. The test allowed for continuous monitoring of the mud consistency not accessible by current measurement practices. The data demonstrated the potential to perform real-time calculation and display of drilling parameters and to detect anomalies in the fluid that might be indicative of developing operational problems, which would enable the instrument to be used as an early-warning system and real-time calculation of drilling parameters. The system detailed here provides an essential building block to enable drilling automation. The robustness and compactness of the instrument allow it to be installed at various points in the mud circulation system for the generation of large data sets that can be processed using modern analytics algorithms in an edge-computing framework.
Numerical Simulation of Effects of Shock Waves on the Integrity of Cement Sheath
Authors: Zhan Guodong David , Xu Jianhui , Aljohar Abdulwahab , Guo Huijuan 
Published: February 2022

Abstract
With the application of electrical explosion technology in the oil industry, such as plugging removal, wax removal and micro-cracks generation near the wellbore zone, the impact of shock wave generated by the electrical explosion on cement sheath cannot be ignored. To study the effect of electrical explosion downhole application on the integrity of cement sheath during the operation, a numerical simulation was used. A composite model of casing-cement sheath-rock is established in AUTODYN. The propagation process of shock waves in the composite and the damage to cement sheath are studied. The results show that the shock wave propagates through the perforation hole in the liquid and keeps a high pressure in the perforated area. In the nonperforated area, the shock wave decays greatly after passing through the casing pipe. When the cement sheath and rock are subjected to the shock waves, the damage area extends outward from the perforation hole. The cement sheath and rock are mainly subjected to the tensile and shear damage by the shock waves. What is more, the casing deformation caused by shock waves may also contribute to the damage of cement sheath behind the casing.
Dynamic Optimization of Underground Gas Storage Reservoir Rate Schedules
Authors: Ilamah Osho , Altwaijri Mohammad Hamad 
Published: February 2022

Abstract
Depleted or partially depleted natural gas reservoirs are attractive candidates for large scale underground gas storage. The effectiveness of underground gas storage reservoirs as a means of addressing seasonal gas demand and supply imbalances depends on optimal storage and withdrawal rate scheduling. In this paper, we develop an approach to maximize seasonal underground gas storage reservoir performance by dynamic optimization of storage and withdrawal rate schedules. Our method involves coupling a predictive gas production and storage system model to a non-invasive population-based optimizer. Optimization is performed progressively on a receding time horizon over which updated optimization requirements and constraints may be admitted. An example application is included to illustrate the approach. The results show that dynamic rate schedule optimization can significantly improve underground gas storage reservoir performance.
Achieving Reservoir Performance Excellence By Implementing Automated Sector Performance: Onshore Field Case Study
Authors: Martinez Santiago Maryvi , Al Shehhi Shamma , Anbalagan Sukilesh , Shbair Alaa , Noordin Firdaus , Trisnadi Dicky , Adli Mustapha , Surya Krisna 
Published: February 2022

Abstract
ADNOC has development and implemented a robust automated sector performance review (SPR) process using state-of-the-art analytics and business process management tool (Khan et al., 2019). In this paper, we will present the achieved results and the defined opportunities by implementing SPR across some targeted reservoirs during the previous last 2 years.
With the necessity of having analysis at sector level, the main objective of this work is to conduct an integrated reservoir dynamic synthesis, identify all challenges and opportunities to come up with robust and practical action plan aiming for best reservoir management and ultimately obtain best oil recovery by sector.
Applying Integrated Reservoir Management (IRM) Workflow on Giant Onshore Field, It was decided to start the project on one major Reservoir A (divided by 3 sectors) as a project pilot.
First data were collected from operations database, data management in spreadsheets, simulation output format, maps and images. All data were organized into the automated SPR workflow through a web based Business Process Management (BPM) that provided mechanism for the user to load, validate and approve technical data.
Setting the workflow to focusing on analyzing the reservoir performance at sector level, the data is illustrated in an integrated visualization environment including panels for the reservoir KPI, production plan compliance status and reservoir pressure maintenance, diagnostic plots, production and injection summary, etc., opening the possibility for the user to identify new opportunities and areas that needs further investigations.
Few key enhancements are listed and were suggested to the solution as a next phase.
Following a methodical SPR automated workflow these conclusion are drawn:
Technical data can be approved with appropriate notification for task execution. Data processing cycles, visualization and performance analysis dashboard time frame was reduced. It was identify the underperforming areas into the sectors. The Opportunity Management proactive system was used to identify reservoir profitable opportunities through a centralized platform. Action plans include well and surface intervention. 20% of the activities were successfully implemented and provided significant added values.
Implementation of the automated SPR workflows as part of Digital technologies is renovating the traditional work process into very effective and advanced analytics and has achieved excellence in reservoir management and reserves recovery.
The Utilization of Machine Learning Method to Predict Hydrocarbon Flow Rate for a Better Reservoir Potential Evaluation
Authors: Kasim Fadzlin Hasani , Idris Nurul Nadhira , Majidaie Saeed , Kantaatmadja Budi Priyatna , Ahmed Siddiqui Numair , Sidek Akhmal , Yahaya Nur Aqilah Nabila 
Published: February 2022

Abstract
The numbers of machine learning technologies used in subsurface characterization work is increasing with more company rely on data driven to assist in performing any evaluation. In this study, a supervised random forest machine learning approach was utilized in two stages; first stage was to predict static reservoir using well logs and core as inputs. The output is then used as the basis in the second stage to predict initial oil rate (Qi) and subsequently to determine estimated ultimate recovery (EUR) at targeted interval as proposed in the first stage.
Static reservoir machine learning prediction outputs were benchmark with available routine core analysis with the result showed R2 of 88% respectively. For initial oil rate (Qi) prediction, a total of 9000 observation points from 20 wells were extracted for training and blind testing process by using variables such as permeability, net thickness, well choke size, well flowing pressure, average pressure, water cut, irreducible water saturation (Swi), and historical production rate. The estimated ultimate recovery (EUR) is then predicted utilizing the thickness of that unit and the decline rate that is obtained from the neighboring wells that has produced from the said reservoir as the analogue. The Qi and EUR results from machine learning is compared with the estimated Qi and EUR using conventional methods for verification purpose.
The results from machine learning dynamic properties prediction showed 97% R2 for training while the testing score mean is 87% against the historical data.
High R2 from static and dynamic machine learning prediction indicated that the method was reliable and able to assist petroleum engineer in reservoir potential evaluation process.
Rigorous Multi-Variate Characterization and Modeling of Paleo Zone for Simulation Model History Matching under Reservoir Uncertainty
Authors: BuKhamseen Nader Y. , Maucec Marko , Awan Anwar R. , Saffar Ali H. , Nieto Jorge E. 
Published: February 2022

Abstract
Presence of paleo zone, which frequently exists below Free-Water-Level surface, can impact dynamic reconciliation of reservoir simulation models. The process is even more challenging with embedded complex representations of reservoir connectivity (conductive fractures) and inherent uncertainty associated with geological and flow modeling. We present a rigorous approach that integrates characterization of paleo zone, parameterization of paleo zone conductivity and application of flow profiles as a guide in accelerated history matching study of large-scale Dual Porosity-Dual Permeability model. The presence of immobile oil within paleo zone can cause permeability reduction and inherently limit aquifer support to oil zone. Accordingly, such occurrence can be represented as a low permeability streak or region in the simulation model and leveraged for more accurate calibration of model injection wells located inside the paleo zone. We performed probabilistic sensitivity analysis and parameterization of paleo zone conductivity using Design of Experiments on a synthetic simulation model with optimized aquifer size and strength as the basecase. The outcome of the synthetic sensitivity scenarios using dynamic model strongly indicates that paleo zone is partially sealing. Multiple scoping runs were performed to identify appropriate permeability values required to calibrate the model. The use of multipliers in porositypermeability transform reproduces blocking or baffling effect of the paleo zone, considering this fluid will behave as part of the rock framework. Porosity and permeability were recomputed inside the paleo zone based on Bulk Volume of Water (BVW) data assessment. The higher the BVW the higher the chance to have effective communication between the oil leg and aquifer. These multipliers represent the probability of the sealing character of the paleo zone and reflect on the non-uniform distribution of accumulated hydrocarbons. Above methodology was used to define the initial set of paleo zone petrophysical property modifiers, rendering multiple model realizations within optimistic-pessimistic range. Flow profiles can be used to guide segmentation of paleo zone with preferential well injectivity to further improve the efficiency of history matching process. Our paper demonstrates a successful application of multi-variate characterization and modeling of paleo zone geometry and properties for a history match of a conceptual, complex reservoir simulation model under reservoir uncertainty. An innovative approach to probabilistic parameterization of paleo zone conductivity has contributed to a model with exceptionally high quality and rendered a reservoir simulation model with reliable predictive capability in accelerated time.
Expediting Well Delivery Through Integrated Drilling Systems Optimization: A Case Study of a Challenging Deep Field in the Middle East
Authors: Ovwigho Efe , Corona Mauricio 
Published: February 2022

Abstract
While drilling vertical deep gas wells, it is required to drill 1300 ft of 5-7/8" section across highly interbedded, abrasive and very hard sandstone formations with compressive strength of 25 – 40 kpsi. This operation is plagued with numerous BHA and bit failures and an average of 3-4 BHA runs are required per well. The current push for expedited well delivery, has made it necessary to optimize the Drillstring in order to enhance performance.
Performed BHA FEA which revealed that the current drillstring was not optimized for drilling long highly interbedded sections. The analysis revealed that the BHA would generate high levels of shocks and vibration that would lead to BHA and bit failue. The model recommended the replacement of all 4" drillpipes by 5-1/2" drillpipes to improve the BHA stability. Optimum drilling parameters were recommended by the model.
Dynamic modelling of the conventional bit also showed that it was not suited for very hard interbedded formations. Introduced high impact rolling diamond cutting element to the conventional bit in order to maintain cutter sharpness for improved drillbit durability. This new cutters were strategically placed across the bit in order to enhance the bit's resistance to impact and abrasion. The conventional motors were also replaced with greater torque motors.
The drilling parameters were also reviewed, revamped and standardized. Operations monitoring team was set up to flag deviations during execution. Wells were assigned for the implementation.
The optimized BHA solution proved to be very stable. While drilling the section, did not observe string stalling, vibrations, shocks or erratic torque. Doubled the on-bottom ROP while drilling this section. The wellbore was in great shape and while POOH to the shoe, no tight spots were recorded and this resulted in the elimination of the planned wiper trip. This solution led to average savings of approximately 140 hours on this well. This consist of 80 hours due to improved ROP, 8 hours due to elimination of wiper trip and 52 hours due to the elimination of two round trips to pick up new BHAs. In addition, the well was delivered six days earlier due to this integrated solution. This solution has been adopted on the Project.
This paper will show how simple changes in drill string design can lead to huge savings in this current climate where there is a constant push for drastic reduction in well costs and expedited well delivery. It will explain the step-by-step process that was followed prior to implementing this integrated solution.
A New Local-Global Upscaling Method for Flow Simulation in Naturally Fractured Reservoirs
Authors: He Xupeng , AlSinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: February 2022

Abstract
Explicit modeling of discrete fractures at the field scale is computationally intensive, and therefore, upscaling the fractures in the context of an equivalent continuum model is indispensable for field-scale simulations. This work introduces a new upscaling technique based on the local-global multiple-boundary (LG-MB) upscaling method for fluid flow in naturally fractured reservoirs.
We extend the commonly-used local-global (LG) upscaling method by introducing a new approach to compute the fluid fluxes within the fractures crossing the grid-block boundaries. This method is based on the multiple-boundary (MB) approach applied within a local-global upscaling procedure. The global coarse-scale simulations are implemented to determine the boundary conditions for the calculations of local upscaled permeability. The procedure allows repeating the process to assure consistency between the global and local calculations. We implement the multi-point flux approximation (MPFA) finite volume method coupled with embedded discrete-fracture model within the MRST framework. We then verify the proposed LG-MB upscaling technique by comparing it with the reference fine-scale solutions and other existing local and local-global upscaling techniques. Results show that the proposed approach provides pronounced accuracy compared to local-based upscaled models with minor computational overhead. Compared to traditional local-global upscaling techniques, it provides more computational accuracy yet with the same efficiency. The superiority of the proposed LG-MB upscaling technique is attributed to two factors related to 1) the multiple-boundary technique to capture the flow behavior with high anisotropy from fractures non-alignment with the grid; 2) the use of local-global approach to accurately obtain the real flow boundary conditions. This work introduces a new upscaling method for flow simulation in naturally fractured reservoirs. We demonstrate its applicability in field applications and its superiority to existing upscaling methods. This method is accurate and straightforward and can be implemented within existing upscaling workflows.
Residual Oil Saturation Estimation from Carbonate Rock Images Based on Direct Simulation and Machine Learning
Authors: Rizk Ahmed Samir , Tembely Moussa , AlAmeri Waleed , Al-Shalabi Emad W. 
Published: February 2022

Abstract
Increasing global oil demand, combined with limited new discoveries, compels oil companies to maximize the value of existing resources by employing enhanced oil recovery (EOR) techniques aimed at the remaining oil. Estimating residual oil saturation (Sor) in the reservoir after conventional recovery techniques, such as waterflooding is critical in screening the suitable EOR technique and in further field development and production prediction. The objective of this work is to provide an artificial intelligence (AI) workflow to assess Sor of carbonate rocks, which will aid in the development of a long-term strategy for efficient production in this fourth industrial age. In the present work, two-phase lattice Boltzmann method (LBM) simulation was used with the benefit of high parallelization schemes. After applying the CPU-based solver using LBM on thousands of carbonate rock digital images, an AI-based workflow was developed to estimate Sor. Different advanced tree-based regression models were tested. Relevant input features were extracted from complex carbonate micro-CT images including porosity, absolute permeability, pore size and pore-throat size distributions, as well as rock surface roughness distribution. These features were fed into the learning models as inputs; while the output used to train and test the models is based on the direct simulation results of Sor from the image dataset.
The results showed that extracting the engineered features from images aided in building a physics-informed machine learning model (ML) capable of accurately predicting Sor of carbonate rocks from their dry images. Three ML models were trained and tested on more than 1000 data points, namely gradient boosting, random forest, and xgradient boosting. Even with such small number of data points, the three models yielded promising results. Gradient boosting algorithm showed the highest predictive capability among the three techniques, with an R2 of 0.71. Increasing the number of data points is expected to help the models capture wider ranges of rock properties, and consequently, result in an increase in the prediction capability of the models. To the best of our knowledge, this is the first study that leverages machine learning to estimating residual oil saturation in complex carbonate. This work will contribute to the development of a novel framework for estimating accurately and reliably residual oil saturation of heterogeneous rocks. As a result, this research will aid in providing decision-makers with a simple tool for screening the most suitable EOR technique for optimal asset use.