bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digital Rock Technology Accelerates Carbonate Rock Laboratory Analysis
Authors: Klemin Denis , Serebryanskaya Assel , Savelev Oleg , Melnikov Sergey 
Published: October 2019

AbstractThis paper describe an application of combined experimental and digital technology workflow for field appraisal. It includes the description of heterogeneous low permeability X oil field located in the southeastern part of the Kurdistan Region of Iraq and its field development planning (FDP) challenges.An integrated laboratory study of low permeability carbonate reservoir rocks (dolomitic limestones) included a digital rock (DR) workflow that accelerated the time to complete core analysis program while bringing vital information about the pore-scale flow dynamics. The DR workflow combined high-resolution digital rock imaging, digital fluid models of reservoir brine and live oil samples, detailed wettability model for sample aging and boundary conditions in digital coreflood experiments. DR imaging spanned from micro-CT for meso- and micropores to high-resolution SEM imaging of submicrometer porosity. Direct HydroDynamic flow simulator was used to model multiphase flow in digital experiments by solving equations of the density functional hydrodynamics (DFH). These equations are conservation laws for the mixture of chemical components, momentum, and energy with constitutive relations involving Helmholtz free energy or the entropy functional.Samples were prepared for DR analysis and their representativeness was verified by obtaining routine properties of original plugs, trims, and mini-plugs selected for high-resolution DR imaging. We established the routine core analysis (RCA) properties of samples using DR and compared them with experimental data. Porous plate digital experiments were performed to obtain air-brine capillary pressure curves on all samples, with DR data verification with laboratory data on selected samples. A set of steady-state (SS) relative permeability digital experiments were then performed with live fluids at reservoir conditions. DR models were first fully saturated with brine and then de-saturated to water saturation that matched reservoir water saturation estimated from well logs. The SS cycle was performed after extended aging to establish a mixed-wet condition. SS relative permeability curves were obtained for all studied samples. DR modeling enabled looking at the dynamic changes of phase saturation in pores and significantly accelerated the laboratory program by performing porous plate tests 100-500 times faster and SS tests 20-50 times faster than conventional analysis using live fluids at pressure and temperature conditions surpassing operating ranges of laboratory equipment.The comprehensive combined study (both laboratory tests and DR analysis) results determined the reservoir flow properties within the entire permeability range. It allowed to reduce uncertainties in predicting production levels, improved the forecast quality of the hydrodynamic model and reduced the difference between the minimum and maximum estimates of geological and recoverable reserves.
Data-Driven Neural Network Model to Predict Equivalent Circulation Density ECD
Authors: Alkinani Husam H. , Al-Hameedi Abo Taleb , Dunn-Norman Shari , Al-Alwani Mustafa A. , Mutar Rusul A. , Al-Bazzaz Waleed H. 
Published: October 2019

Abstract
Equivalent circulation density (ECD) is vital in drilling operations. Poor management of ECD can lead to many drilling obstacles that can increase the delivery time of the well. The aim of this study is to utilize artificial neural networks (ANNs) to create a model to estimate ECD prior to drilling.
Data of more than 2000 wells collected from multiple sources worldwide were utilized in this work. An ANN model was created with one hidden layer and 12 neurons in the hidden layer. The data were clustered into three data sets; training (70% of the data), verification (15% of the data), and testing (15% of the data). Ten training algorithms were utilized to train the network, the training algorithm with the lowest mean squared error (MSE) and the highest R2 was selected to achieve the best predictive model. Bayesian Regularization (BR) algorithm was selected to train the model because it had the highest R2 and the lowest MSE.
The results showed that the created model can predict ECD within an acceptable margin of error. The overall R2 of the model was 0.982 which is considered very good. Alternatively, given a target of ECD, the created model can be utilized in reverse to obtain the desired ECD by altering the key drilling parameters affecting the ECD model (the inputs). This will help the drilling personnel to optimize ECD in the field.
Intelligent systems and machine learning have proven their effectiveness in solving complicated problems that cannot be solved analytically. With the large historical drilling data available in the oil and gas industry, machine learning and intelligent systems can be used to make better future decisions that will help to optimize the drilling operations.
Artificial Intelligence Opportunities for Environmental Protection
Authors: Hojageldiyev Dayanch 
Published: October 2019

Abstract
Artificial Intelligence systems utilize a variety of technologies making it possible to produce comprehensive, nonetheless easy-to-use tools, which can be used by Health, Safety and Environment professionals in oil and gas industry. The objective of this paper is to provide an overview of AI opportunities for constantly evolving oil and gas business in addressing such issues as environmental protection.
This paper examines a variety of AI applications aimed to provide support to HSE professionals in reviewing big batches of random data and identify important patterns for further planning; develop viable models to assist examination of various event scenarios, predict and mitigate emergencies. As with any data-driven AI tool, the type and amount of data available are perhaps the decisive factors when selecting the AI agent environment. The HSE fields with ample amounts of workable data for machine learning will be reviewed.
AI systems utilize a variety of technologies making it possible to produce comprehensive, nonetheless easy-to-use tools, which can be used by HSE professionals in their daily activities. Artificial intelligence technologies today offer a pool of decision-making models for oil and gas industry. The tools developed based on the AI technologies and machine learning can help specialists working in the fields of HSE to advance their efforts in environmental protection, and contribute to mitigating climate change and weather disaster issues, biodiversity conservation, waste reduction, water security, healthy oceans, and clean air. There are over 80 areas in environmental protection initiatives where the AI and ML models are actively used. These tools prove to be more effective in executing routine tasks with higher accuracy and faster completion rates. The AI is also progressing in unsupervised decision-making, which however, raises concerns among the scientists and law-makers as algorithms using historical data may not only incorporate previous discriminations and biases, but reinforce them.
Artificial intelligent systems using machine learning are constantly improving in accuracy, presenting an opportunity for constantly growing oil and gas business in improving their environmental compliance activity, and developing a well-praised image of a sustainable community partner.
Comprehensive Evaluation of Water Breakthrough With a Novel Method to Estimate Water Production
Authors: Al Shehri Dhafer 
Published: October 2019

Abstract
Produced water is inevitable in the life span of a reservoir despite the challenges associated with it. To make an informed decision on economic and engineering design, it is essential to establish post water breakthrough trend to predict water production and to design surface facilities to handle the volume of water that will be produced. Several correlations and models have been proposed in the literature; however, each of the models could solve the problem from which it was derived. Most of the traditional models represent water cut as a function of cumulative oil production or time which is only achievable when accurate water cuts are available.
This paper analysis post water breakthrough performance using empirical models. In this proposed method, the water cut (WC) was redefined and plotted against the oil flow rate (Qo). Different models were developed to capture the trends of the plotted graph. It is interesting to note that during water breakthrough, there exist exponential, hyperbolic and harmonic increase in water production throughout the reservoir life span. The model was validated with producing field data and has proven to be robust and can be a reliable tool for estimating water production.
The newly developed model in this paper offers an advantage of predicting the corresponding WC at any given Qo and bottom hole pressure.
Petrophysical Evaluation Challenges in Shallow Gas Reservoir Deposited in Sabkha Environment, Case Study, UAE
Authors: Abdelaal Atef , Hanaee Ahmed Al , Taher Ahmed , Deminova Anna , Khan Riaz , Kurniawan Redy , Vantala Aurifullah , Akl Osama 
Published: October 2019

Abstract
The petrophysical evaluation for Sabkha environment is very challenge due to high reservoir heterogeneity and complex mineralogy in addition to existing of multi-lamination that have various thickness ranging from 1 to 20 ft., also these kinds of reservoirs normally have very high formation salinity that impact as well the water saturation calculations.
In 2018, a unique worldwide biogenic shallow gas reservoir was discovered in Onshore Abu Dhabi, UAE in Miocene Sabkha deposits while drilling a dedicated well for deeper reservoir, the average reservoir gross thickness is about 4500 ft. with hundreds of intercalations from Clay, Limestone, Dolomite, Marl, Anhydrite and Halite.
This paper presents the detailed challenges for such reservoir regarding the petrophysical evaluation (including but not limited to porosity calculations, water saturation evaluation by different methodology, net to gross ratio, log and core data integration, definition of high potential spot for testing, etc..) Moreover, the way to manage and mitigate these challenges in addition to testing technology and results.
So far Five dedicated exploration wells were drilled in this reservoir at which a complete set of logs were acquired in addition to collecting of core footage about 4560 ft. from two wells and analysis is ongoing, also Rigless testing was done for 3 wells. In general, based on collecting data the reservoir properties showed very tight reservoir (permeability &lt; 0.01 md) with high mineralogy complexity as well as high formation salinity (+ 300 kppm). Good Total Organic Carbone is also measures across the reservoir that confirmed the ability to produce Microbial gas.
As a results of the integrated study, the sweet spots across the reservoir were identified and tested, accordingly placement for horizontal development wells as well as stimulation technology are optimized
Proposing a New Eco-Friendly Drilling Fluid Additive to Enhance the Filtration Properties of Water-Based Drilling Fluid Systems
Authors: Al-Hameedi Abo Taleb , Alkinani Husam H. , Dunn-Norman Shari , Al-Alwani Mustafa A. , Alshammari Abdullah F. , Albazzaz Hussien W. , Alkhamis Mohammed M. , Rusul A. Mutar , Al-Bazzaz Waleed H. 
Published: October 2019

Abstract
The main objective of this paper is to develop a new material used to control the seepage losses and combat issues regarding thick mud cake. The plan is to develop a biodegradable waste material from Grass, which is environmentally friendly to be utilized as an alternative material to address filtration problems. Raw material from local Grass in the USA was used to construct the fibrous fluid loss control agent (Grass Powder). The lab procedure consisted of crushing and grinding process to assemble the samples to be ready for preparation. Spud mud samples were used as a guide to indicate filtration control. The material was tested to generate results from the experiments conducted at surface conditions to determine the reduction in the volume of filtrate and mud cake. Based on the experiments conducted, Grass Powder (GP) can be applied to treat problems associated with the seepage loss in the drilling operations. The grass availability, being eco-friendly, low cost, and the simple method of Grass Powder preparation by using grinding and crushing can prove to be a suitable replacement for conventional materials used to control filtration.
Then, the results of Grass Powder were compared to starch, commonly used conventional additive. The results showed that Grass Powder decreased the fluid loss by 44% at 1% (7 grams) concentration of GP, and the filter cake was enhanced as well when comparing it to the reference fluid. While the starch material showed an improvement in seepage loss by 40% at 1% (7 grams) concentration when comparing it to the reference fluid. In addition, starch was less efficient in improving the filter cake as compared to Grass Powder. Consequently, GP laboratory outcomes slightly showed better performance as compared to starch additive, suggesting the feasibility of Grass Powder to be used as fluid loss control agents.
In brief, these experimental results exhibit that Grass Powder has the potential to be utilized as biodegradable drilling fluid additives replacing chemical additives and reducing the amount of non-biodegradable waste disposed to the environment.
How Engineering Design Enabled Drilling the Longest Intermediate Section
Authors: Galaby Amir , Cardenas Miguel , Cengiz Husnu Ozgur , Kosberg Birgitte Ruud , Sanderson Martin , Krikor Ara 
Published: October 2019

Abstract
As the demand for natural gas continuously increases to meet electricity production needs, more alternative natural gas sources are usually required to cope with the increased demand in the summer months. In South Iraq, this situation is the main driver for exploiting economically feasible and efficient solutions in finding additional natural gas resources. As a result, the Iraqi government embraces the challenge by drilling deeper formations which are gas bearing, with limited experience in such fields. In the most recent appraisal campaign for these gas fields, performed in 2015 and 2016, one of the main challenges were bits and bottom hole assembly (BHA) failures while drilling through different interbedded and abrasive formations. Changing the bits type, BHA centralization, drive system and drilling parameters did not result in significant benefits.
For the 17 ½" section, up to six (6) independent runs were required to successfully drill the entire section. The success was limited due to severe shocks and vibrations, high axial forces and high torque that caused failures on downhole mud motors and bits, leading to fishing operation and severe non-productive time. Other problems like unstable drilling parameters, extremely low rate of penetration (ROP) and poor wellbore quality were observed, leading to excessive reaming and backreaming as well as stuck pipe events and difficulties to maintain well verticality. To avoid and minimize the impact of these challenges, a comprehensive engineering team performed various finite element analysis on the interaction between BHA, drilling bit and formations drilled. The conditions on which the wellbore was maintained in the offset wells with its drilling fluids strategy and drilling parameters were reanalyzed. The bits selections were revised, identifying areas of opportunity to introduce fit for purpose technologies on cutters and bits profile. The ultimate challenge was to drill the full 1800 m of the section in one run avoiding any BHA related failure.
The results exceeded expectations in the fourth well, where no BHA related failures were observed, and the drilling bit was able to drill the full 1800 m. Connection practices were also optimized. This enabled an improvement in the wellbore quality based on caliper logs. Improved wellbore conditions allowed a smooth casing run and consecutive cement job. This paper will discuss the engineering methodology followed to achieve this important milestone in one of the few gas fields in Iraq. It will go through the details of the technologies implemented on BHA analysis, bit selection, drilling parameters optimization and drilling fluids strategy implemented.
The objective of this paper is to share with the oil and gas industry a methodical approach for efficient drilling, and how to address drilling challenges with technology introduction and engineering design.
Taking A Nap: Passive EOR Implementation for Resilient Management of Cost and OPEX for A Polymer Flood Project with Horizontal Wells in South Sultanate of Oman
Authors: Al-Jumah Ali , Saadi Faisal , Harthy Khalfan , Lawati Sakeena , Khaburi Saja , Battashi Fathiya , Al Aghbari Dareen , Harthi Mahmood 
Published: October 2019

Abstract
This paper will discuss the evidence gathered proving the presence of Nothing-Alternating-Polymer (NAP) mechanism, in a current Polymer flood pattern during injectors offline periods, and the extension project designed to test and control NAP in conjunction with a nearby pattern in a horizontal wells environment. This project will help unlock a pragmatic Fullfield development, and highlight the associated benefits from a project’s perspective, leading towards huge savings and flexible operating conditions. Practical considerations and suggested operational methodology is also discussed.
AI-Based ESP Optimal Control Solution to Optimize Oil Flow Across Multiple Wells
Authors: Krikunov Dmitry , Kosyachenko Semen , Lukovkin Dmitry , Kunchinin Alexander , Tolmachev Roman , Chebotarev Roman 
Published: October 2019

Abstract
Objectives/Scope: The proposed paper will describe the results and operation principles of the developed AI-based solution for the optimization of operating modes of electrical submersible pumps (ESP). The solution allows exploration and production companies to increase the oil flow rate from ESPs without additional capital investments. The solution is based on the technical possibility of ESP mode adjustments in a certain range of motor frequencies and control types.
Methods, Procedures, Process: The hypothesis is that there is a certain ESP operation mode in each period of time which ensures the maximum daily oil and gas flow rate in the long term. The proposed solution uses statistical methods to ensure data sufficiency and stability and employs custom algorithms to conduct problem-specific data preprocessing. The "well-reservoir" model is the combination of physical (i.e., pump model) and machine learning (ML) models. The ML part of the solution was designed for the short- and long-term predictions of well-reservoir system response. However, the model faced constraints such as current frequency, load and amperage.
Results, Observations, Conclusions: The following results were delivered: data collection and preprocessing pipeline was built, a hybrid physical-ML prediction model was designed and trained, and a numeric optimization model which uses outputs of the hybrid prediction model was designed to recommend multi-well operation modes. The solution has been successfully deployed and tested for 6 months on 500 oil wells in Western Siberia. The designed ESP optimal control solution could enhance the efficiency of oil extraction by boosting oil well production rates by 1.5% with no additional capital investment, as proven during the production phase. Thus, the proposed "reservoir-well-pump" system modeling approach showed efficiency and superiority over the previous control methods used by oilfield operators.
Novel/Additive Information: To the authors' best knowledge, the present paper is the first work covering research, development and production testing of hybrid physical-ML methods in the optimal control of ESPs with respect to optimization of oil flow. Application of some known statistical methods to the problem of provision of reliable data to the model is additive to the current body of knowledge.
Modeling Fluid Flow in Tight Unconventional Reservoirs: Nano Scale Mobility/Trapability Mechanistic Approach!
Authors: Belhaj Hadi , Qaddoura Rabab , Ghosh Bisweswar , Saqer Rana 
Published: October 2019

AbstractFluid flow through UCRs (Unoconventional Reservoirs) is governed by several mechanisms such as viscous, convection (comprises of diffusion and advection), sorption (comprises of absorption and adsorption), capillary and inertial forces taking places in two different domains - inorganic (rock) domain and organic (kerogen) domain. These mechanisms act in two main pore scale levels, Micro- and Nano-pores, in addition to the Macro scale level considered to host the flow within fractures and fissures whenever encountered (naturally existing or induced). This research aims at building a comprehensive model, based on thorough understanding of these fluid flow complexities of UCRs. The developed modeling technique assumed to predict fluid flow by coupling the effect of both displacement and trapability forces and relating these forces within the three pore scale levels (Macro, Micro and Nano). A fundamental model for the above mentioned mechanisms has been established. Sensitivity study to determine the contributions of these specific mechanisms along with a criterion to develop tight UCRs is presented.A new and unique flow behavioral model to enable predicting flow characteristics through tight UCRs within nanopores has been established. This model will be used in estimating realistic hydrocarbon-in-place, reserves, and profiling production performance using different potential methods. This research has the following specific outcomes: (1) Comprehensive workflow and methods of UCR characterization. (2) A model to predict the fluid flow behavior through UCRs. (3) A criterion to predict the effects of different flow parameters (displacement & trapability) on fluid flow behavior in UCRs. (4) A new field-development scheme suitable for tight UCRs.