bluuebunny/crossref_metadata_2025_split huggingface dataset:
Curing Losses with Bio-Degradables: Challenges and Feasability of Particle Size Distribution
Authors: Omer Mohammed , Fragachan Francisco 
Published: January 2020

Lost Circulation currently accounts for loss of approximately around $850-900 million USD per year. Middle East is probably counting for a major part of these losses. This research paper reviews the current practices of LCMs, different polymeric materials used, and this paper will explore new ideas revolving around biodegradables as part of our commitment to make the industry cleaner and greener.
Drilling of Oil wells is encountered with a host of technical problems that often makes it difficult to develop some of the fields. Lost circulation is one such critical issue defined as the continuous loss of drilling fluid when it encounters fractures, vugs, highly permeable zones during drilling. If not controlled Lost Circulation can lead to stuck pipe issues, wellbore stability issues and ultimately can lead to blowout endangering life and property.
Despite several technological advances in this area, Lost Circulation Materials (LCMs) today has many drawbacks such as damaging the formations, failing to seal the fractures and damaging the environment. This paper presents the feasibility of various lost circulation materials and explains the physics behind the particle size distribution necessary to have an effective sealing and jamming. An analytical model based on CFD-DEM (Couple Flow Dynamics & Discrete Element Modelling) has been developed, calibrated with experimental results and existing models to optimize the parameters. It discusses about the need of new engineering for designing LCM success performance
We are focusing on possible use of plant based degradable renewable materials which degrades back to liquid state as a potential lost circulation material in this paper. The engineering analysis will focus on optimizing the LCM in relation to fracture dimensions.
Emerging Fluid Design with Enhanced Proppant Carrying Capacity: An Integrated Geomechanical Workflow
Authors: Perez Oswaldo , Fragachan Francisco E. , Omer Mohammed , Huang Jian 
Published: January 2020

The overall success of a hydraulic fracturing treatment and the resulting fracture properties, such as length, height extent, and conductivity, are dictated by the proppant -transport- distribution and its mechanical behavior -effectiveness- within the stimulated fracture network. With the goal of minimizing (eliminating) proppant settling and optimize required horse power on location reducing equipment foot print and volume of water required; an engineered low-viscosity fluid with high proppant carrying capacity has been implemented to improve proppant transport and zone coverage in stimulated fracture networks. An integrated methodology to optimize the emerging fluid design and proppant placement, is presented.To evaluate and quantify the efficiency of the proppant transport process with a novel low viscosity fluid, an integrated geomechanics workflow was developed combining quick look analysis (i.e. candidate selection) with advanced computational models (i.e. geomechanical models) to improve proppant deliverability. Experiments were conducted to quantify the shear thinning behavior and proppant carrying capacity of the engineered fracturing fluid under different flowing conditions. By honoring experimental results, multiple analytical and numerical models/modules were developed and utilized within the framework of the workflow to assess the designed efficiency.The novel fracturing fluid is characterized as low viscosity (comparable to that of linear gel), yet it can suspend and transport conventional proppants similarly to a high-viscosity fluid (e.g. crosslink gel), decoupling the need for viscosity with proppant transport. Our analysis indicates that the fluid design (viscosity & proppant carrying capability) and proppant selection type should be customized and evaluated based on the local GeoMechanical conditions. If engineered accurately, fit-for-purpose, fluid can effectively distribute proppant into the fracture surface area, sustaining closure stresses, reducing embedment, and achieving longer effective fracture length(s) and larger conductive reservoir volume with enhanced production.The formulated workflow can provide an optimized design or improve existing designs based on the reservoir properties and field limitations by iteratively optimizing relevant controls (such as fluid design, proppant type, pumping schedule) of a fracturing operation. Our engineered fluid (EF) technology together with a proposed workflow, demonstrates that fluid design and proppant placement and operational parameters can be customized to maximize production, without compromising for settling and zone coverage.
A Multistep Reactive Convection Model for the Prediction of the Dolomite Bodies Distribution in the Karachaganak Field - Kazakhstan
Authors: Gabellone Tatyana , Borromeo Ornella , Consonni Alberto , Geloni Claudio , Albertini Cristian , Bigoni Francesco , Francesconi Arrigo , Lazzeri Riccardo , Vercellino Alberto 
Published: January 2020

In the Carboniferous reservoir of the Karachaganak field the occurrence of replacive dolomite improves the reservoir quality. Petrographic and geochemical data on core samples suggest a geothermal convection along the slope as the main dolomitizing mechanism. Despite the significant number of wells, inferring the 3D distribution of these dolomitized bodies from lithological logs analysis only is quite challenging. To improve the understanding on the dolomite spatial distribution, Reactive Transport Modelling (RTM) at field scale has been performed with an in house developed software (MuFloT). MuFloT is a graphical environment designed to perform non isothermal multiphase flow and reactive transport models through a customized version of the TOUGHREACT commercial code.
Dolomitization by geothermal convection in the shallow burial domain was simulated on a 3D grid at the field scale in three discrete steps, following the stratigraphic architecture consisting of an aggrading phase (Layer 1), a prograding phase (Layer 2) and an upper prograding unit dominated by cyclic platform deposits (Layer 3). The geometry of the three layers was obtained by decompaction and tilting of the present-day horizons from the reservoir static model. The grid was populated with five rock domains characterized by different porosity and permeability, which were inferred by extrapolating present-day values to depositional conditions.
The specified heat flux at the bottom boundary in each step generates a heterogeneous temperature distribution with higher temperatures up to 47 °C reached in the subsurface of the platform interior. The temperature difference between the warm platform interior and the colder flanks generates convective half cells causing a seawater influx from the flanks of the platform. This geothermal convection triggers the replacement of calcite by dolomite. Dolomite formed in each step acts as a seed for additional dolomite formation during the subsequent episodes of geothermal circulation. Simulation of all the three Layers (4 Myr in total) drives complete dolomitization on the slope areas in Layers 3 and 2, in agreement with observations from well logs and cores. The modelled spatial distribution of dolomite provides trends and rules for the distribution of lithology and reservoir properties in the reservoir model.
Approach for Wellbore Production Monitoring Using Distributed Acoustic Noise Measurements
Authors: Miklashevskiy Dmitriy , Shako Valery , Borodin Igor , Wilson Colin , Kortukov Dmitry , Tarelko Nikolay , Zozulya Oleg 
Published: January 2020

The objective of this study was to develop a data processing flow for the oil and gas industry enabling the determination of inflow profile and fluid type identification for in well oil-water flows, and to quantify the phase and flow rates using distributed acoustic vibration data, in a near real-time wellbore monitoring scenario. The implementation of the approach will enable alarms to be raised in real-time in zones when changes in the flow rates and phase changes exceed predetermined levels, allowing quantifiable operational decisions.
The acoustic data was acquired using a distributed fiber optical (FO) sensing technique and reference hydrophones in a laboratory flow loop equipped with accurate reference flowmeters. The acoustic noise created by the main pipe flow and inflow was studied. Total flow rate varied within the range 0 to 200 m3/d for water and a model of light oil.
A set of numerical models was used to support development of the interpretation approaches through an enhanced understanding of the acoustic field in various real wellbore geometries.
Two interpretation approaches based on laboratory correlations of acoustic noise energy in a selected frequency range and machine learning algorithms were developed to quantify phase rates from distributed acoustic vibration data induced by turbulent fluid flow in laboratory conditions.
It is shown that correlation-based interpretation enables flow quantification and profiling within acceptable uncertainty levels, for a field dataset of distributed vibration measurements and a reference production logging tool (PLT) log in a producing wellbore.
The goal of the software development is to provide a quantitative flow characterization from the interpretation of distributed acoustic vibration measurements. This method was tested using field fiber optic datasets combined with reference PLT log data.
First Worldwide Implementation of Pressurized Mud Cap Drilling for a 17 × 19 in. Hole Section Translates into Safer and More Cost-Effective Operations
Authors: Al-Otaibi Rakan F. , Al-Haqwi Mohammed , Iturrios Carlos O. 
Published: January 2020

During the development of drilling operations there are several operational risks and hazards that can jeopardize the continuity of the operation or make it so complicated that the economic burden to drill becomes unacceptable.Loss of circulation is one of the main causes of cost increase in drilling operations throughout the world and often the formation characteristics, the well design, or the available technologies make it impossible to eliminate this problem.Studies have shown that drilling fluids represent between 25% and 40% of the total cost of drilling a well . With this in mind it is clear that encountering loss of circulation, and particularly total losses, represents a significant impact on the economic aspect of the operations. Additionally, several cases face the problem that operations cannot continue unless the losses are stopped.
Engineering Design of Multistage Acid Fracturing Operation in a Dual Lateral Well
Authors: Al Jabr Abdullah , Nwachukwu Cosmas , Al Hajri Faleh , Al Nasser Mohammed 
Published: January 2020

This paper describes the planning for an application of multi stage acid fracturing in a dual lateral well. It also highlights associated risks and methods to be used for risk mitigation.
Multi-Stage Fracturing (MSF) is considered to be very challenging in dual lateral wells due to the challenges arising from (1) managing the losses after fracturing without damaging the reservoir and (2) isolating cemented junction (known as TAML level 4 junction) from high pressure required for fracturing and stimulation operations.
The unique application of existing technologies such as flapper ceramic valve used in the mother bore and dissolving ball with ball seat installed in the upper lateral is a feasible solution to the challenge of severe losses.
Moreover, the utilization of frac string equipped with seal assembly that stings into an extended Polished Bore Receptacle (PBR) helps in protecting the junction from high stimulation pressures.
These technologies have been tailored to meet the application requirements. The success of this application will encourage exploring the potential for multi stage fracturing in more complex multi-lateral wells, such as tri-laterals, creating a positive impact on petroleum industry by enhancing production and achieving higher profitability from these wells with potential of reducing the number of wells to be drilled to achieve a desired production rate.
Understanding the Concurrence of Drilling Induced Fractures and Breakouts and Its Implications on Drilling Performance Using a 3D FEM and Field Imaging Data
Authors: AlBahrani Hussain , Morita Nobuo 
Published: January 2020

The drilling mud weight window addresses two modes of wellbore rock failure which lie on opposite ends. Breakouts being near the minimum limit and fracturing at the maximum. However, as evidenced by image logs, it's not uncommon to identify breakouts and induced fractures taking place within the same interval. Exploring this interesting phenomenon should lead to better understanding of how the window changes. This should also serve to identify near misses preceding drilling troubles.
In this work, field data are first used for the diagnostics of this phenomenon and to verify the order of occurrence of each mode of failure. Different forms of image logs taken at different time intervals are used for this purpose. Having established the values of relevant variables such as mud properties, rock properties, and wellbore dimensions, a 3D finite element model is then used to quantify the changes in the drilling window as each mode of failure starts to take place.
It is observed that while fluctuations in the bottomhole pressure during the different drilling phases could be a starting point to understand this phenomenon, it's not sufficient. This is especially apparent when considering the size of the window or the various measures taken by modern day drilling practices to maintain the bottomhole pressure within the window. Understanding the order of each failure occurrence and their impact on each other serves to accurately identify the onset of a host of drilling troubles such as lost circulation, poor hole cleaning, and stuck-pipe incidents. This will essentially work to produce a first attempt at estimating the life cycle of the drilling window throughout the different drilling phases with the end result being a dynamic definition of the window. The outcome of this work presents a new perspective through which, different drilling variables can be manipulated to eliminate the onset of drilling troubles. Based on this, it can be shown how the allowable mud weight is evolving from its base range as the wellbore begins to deform. The implications of these changes of the drilling window can be reflected on other peripheral variables such as surge and swab tolerance and allowable equivalent circulating density.
The results of the model can be used to insure more efficient employment of bottomhole pressure maintenance techniques such as managed pressure drilling. Also, in a system where the required real-time data gathering equipment is available, the model provides a path towards automating the responses to the constantly changing environment downhole.
A New Method to Quantify Interwell Connectivity Using Performance Data and Intelligence Algorithm
Authors: Liu Xue , Qu Xiangyun , Jiang Ming , Huang Jing , Chen Cunliang 
Published: January 2020

Interwell dynamic connectivity is one of the important indicators for development evaluation of water flooding oilfields. It is widely used in identification of dominant channels, judgement of fracture sealing and evaluation of development effect. The traditional capacitance-resistance model (CRM) can not consider the change of start-up pressure gradient and liquid production index of heavy oil reservoir. For better quantifying the interwell connectivity of heavy oil reservoir, a new method is proposed using performance data and intelligence algorithm.
Experiments show that there is a starting pressure gradient in heavy oil flow. The pseudo-start pressure gradient model is used to characterize this phenomenon. On this basis, the fluid production model of heavy oil reservoir is deduced and established. The results show that the liquid yield index increases with the increase of water content, which is not a constant. And then, a time constant function is constructed to characterize the lag and attenuation of water flooding signals in formation propagation. By substituting the production model and the time constant function into the equation of material balance, a new model is established by difference method. The solution of the new model is transformed into an optimization problem by using the least squares principle. And the quantitative connectivity evaluation is obtained by using frog leaping algorithm.
The new model has been applied in many oilfields. Compared with the single-well dynamic analysis, the accuracy of the new model is very high, but the spent time is less than half of the single-well dynamic analysis. In addition, the paper presents a case study to compare findings from the results of the new model and the use of interwell tracer and interference well testing. The results obtained from this paper shows good agreement with the results obtained from interwell tracer or interference well testing. However, compared with the tracer test or interference well testing, the new method can save a lot of money. In summary, this paper is not only feasible, but also saves a lot of time and money.
The new method considers not only the starting pressure gradient of heavy oil, but also the change of liquid production index. It is an effective method for evaluating injection-production connectivity.
Virtual Metering and Allocation using Machine Learning Algorithms
Authors: Sanzo' Simona , Montini Marco , Cadei Luca , Giuliani Marco , Bianco Amalia 
Published: January 2020

In this paper a machine learning algorithm based on neural networks is proposed. The aim is to test a model for multiphase flow rates estimation, taking into consideration the process parameters and the performance of the installed equipment. This model can work in parallel with the Multiphase Flow Meter (MPFM) measurements or acts as a back-up when it fails or it is not present.
The model mainly consists in a multilayer feed-forward neural network, suitable for non-linear regression problems such as the one here considered. The model solution takes a set of reservoir and process parameters as inputs and returns a set of fluid flow information as outputs. The model is trained using actual well test data from producing wells. Generalization and network complexity are regulated using cross-validation. In addition, the mean square error is used as the performance function for training the feed-forward neural network.
The predictive capabilities of the neural network for estimating oil and gas flow rates in multiphase production wells have been assessed against field measurements collected from several production wells operating in west Africa. The developed machine learning model shows promising results and a high level of accuracy, with predicted output very close to the actual MPFM measurements for oil and gas flow rates for specific data considered in the present analyses. Thus, the model can be applied to predict multiphase flow rates in different wells and acts as a back-up when MPFM fails or is not present. The great advantage provided by this model is its inherent simplicity and the small computational time taken to provide an output. Therefore, it can also be thought as a valid alternative to virtual flow metering techniques that are based on physical models and are highly dependent on variables with a high degree of uncertainty (e.g. fluid properties).
This work presents a promisingapproach for virtual metering technique. The methodology has possible application in real time monitoring but also in production optimization, production allocation and reservoir management and it is a cheaper alternative with respect to MPFM, reducing in this way operational and maintenance costs.
Simulating the Behavior of Reservoirs with Convolutional and Recurrent Neural Networks
Authors: Alakeely Abdullah , Horne Roland N. 
Published: January 2020

Recent experience in applying recurrent neural networks (RNNs) to interpreting Permannt Downhole Gauge (PDG) records has highlighted the potential utility of machine learning algorithms to learn reservoir behavior from data. The power of the RNN resides in its ability to retain information in a form of memory of previous patterns and information contained in the previous behavior of phenomena being modeled. This memory plays a role of informing the decision at the present time by using what happened in the past. This property suggests RNNs as a suitable choice to model sequences of reservoir information, even when the reservoir modeler is faced with incomplete knowledge of the underlying physical system.
Convolutional Neural Networks (CNNs) are another variant of machine learning algorithms that have shown promise in sequence modeling domains, such as audio synthesis and machine translation. In this study, RNN and CNN were applied to tasks that traditionally would be modeled by a reservoir simulator. This was achieved by formulating the relationship between physical quantities of interest from subsurface reservoirs as a sequence mapping problem. In addition, the performance of a CNN layer as compared to RNN was evaluated systematically to investigate their capabilities in a variety of tasks of interest to the reservoir engineer.
Preliminary results suggest that CNNs, with specific design modifications, are as capable as RNNs in modeling sequences of information and as reliable when making inferences to cases that has not been seen by the algorithm during training. Design details and reasons pertaining to the way these two seemingly different architectures process information, and handle memory are also discussed.
Porous Limestone Formation Stimulation Using Deep Penetrating Acidizing Technique for Both Injectors and Producers
Authors: Lu Yan , Wang Da , Liang Jiabo , Feng Puyong , Shao Shangqi , Wang Chao 
Published: January 2020

Matrix acidizing is commonly used to enhance the production of damaged wells. As to undamaged wells, it is commonly believed that matrix acidizing can hardly enhance their productivity or injectivity. In Iraq X Oilfield, the acidizing operations for four undamaged wells have failed to enhance their performances, which seemed to meet this assupmtion.
A novel deep penetrating long wormhole creation acidizing technique has been proposed and tested in X Oilfield to stimulate these undamaged wells. In comparison with averaged acidizing technique, it can create a much deeper dominant wormhole by applying a much larger acid volume and pumping rate so as to further enhance the well's productivity or injectivity. Very important, it applies a new type of solvent that can better remove asphalt from the rock surface to facilitate the rock-acid reaction and a retarded acid system to make sure the acid can go deeper into the formation.
This novel technique has been tested in all of the fore-mentioned three production wells and one injection well, which have been failed by averaged acidizing technique before. For these three production wells, the production enhancement ratio after using averaged acidizing technique was only 7% on average. However, the well test result for one of these wells showed that despite the low production enhancement, the post stimulation skin factor is −2.08, which showed that the near wellbore pollution had already been removed by the average acidizing technique. Then they were treated with deep penetrating acidizing technique and the production increased correspondingly. In addition, for the injection well, after averaged acidizing technique treatment, the injectivity index quickly decreased from 252m3/(d.Mpa) to 95m3/(d.Mpa) in only two months. However, after the deep penetrating technique operation, the injection pressure stabilizes at 0 injection pressure for more than 9 months with the same injection rate.
This new technique has been the first time tried in this porous limestone formation to stimulate undamaged wells after the failure using averaged acidizing technique. Though first tried in this Iraqi Oilfield, it can be extraopolated to other oilfields with similar limestone formation around the world.