bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deterministic or Probabilistic Approach for Mature Oilfield Development: Isn't it all about Decision Making?
Authors: Yadav Atul , Omara Essam , El-Hawari Ahmed , Malkov Anton , Bisso Bi Mba Edna Michelle 
Published: October 2018

Abstract
The integrated reservoir management study was initiated to identify the best fit for purpose dynamic model for decision making with increased confidence in production forecasts and proposed field development plan. The mature oil field with complex geology and huge uncertainties in production allocation is located in the Gulf of Suez. These conditions made it necessary to evaluate and select the best suited approach between the deterministic and probabilistic approach.
Considering the geological complexity and field performance behavior, integrated reservoir modeling approach was selected to achieve objectives of the study. During that process, the screening for the best suited methods such as probabilistic or deterministic was made. Due to the heavily faulted and compartmentalized nature of the field with an incomplete understanding of probability ranges and relationship between different variables none of the approaches is perfect.
The acceptable field history match was achieved to serve the basis for future field development and to increase the current production rates. The chosen deterministic approach illustrates how the global and regional uncertainties can be narrowed down by consequent analysis and inclusion of observed well data such as RFT, PLT, production water salinity, tracer data etc. The accurate estimation of remaining production potential in different reservoir units was delivered. The promising prospects in the eastern blocks adjacent to the main producing field were identified. The estimated incremental oil production within next 5 years is around ~10 MMSTB. Above mentioned results were considered as a basis for the license extension which was about to expire in the year 2017. This integrated approach was helpful for understanding the difficulties in complex oil field simulation study, knowledge sharing between management of joint partners, G&G experts, and reservoir engineers.
The upstream industry projects demand the optimal solutions for business decisions under prevailing uncertain conditions. The presented case study provides a practical approach to achieve it. The lessons learned from the study may give a guidance to similar mature field development in the region and worldwide.
Increased Oil Production in Super Thin Oil Rim Using the Application of Autonomous Inflow Control Devices
Authors: Mohd Ismail Ismarullizam , Che Sidik Nor Azuairi , Syarani Wahi Faez , Tan Giok Lin , Tom Focht , Hillis Frazer 
Published: October 2018

Abstract
Despite the improved productivity and uplift in reserve recovery associated with horizontal wells, reservoir heterogeneity can cause uneven production, and early water and gas breakthrough from portions of the wellbore. Inflow control devices (ICD) create additional pressure drop to balance the production flux, but cannot restrict unwanted effluents once they break through.
The Autonomous Inflow Control Device (AICD) actively delivers a variable flow restriction in response to the properties (viscosity) of the fluid flowing through it. Water or gas flowing through the device is restricted more than oil. When used in a horizontal well, segmented into multiple compartments, this design prevents excessive production of unwanted effluents after breakthrough occurs in one or more compartments.
In the 2016 infill development campaign, production was improved by AICDs to ensure contribution from all reservoir sections, and limit gas and water production by postponing breakthrough and restricting unwanted effluent production after breakthrough.
A nine well program was selected to demonstrate the effectiveness of AICD completions in the East Belumut and West Belumut assets, a field development offshore Malaysia. The wells are drilled with horizontal lengths typically 1.5km within the original 6-8m thin oil column for West Belumut and 10-14m thin oil column for East Belumut. The program comprised of AICD flow loop testing, performance modelling, candidate selection, completion design and comparing production results with neighbouring ICD wells in the fields.
The implementation of an AICD completion was a success and full fields implementation took place in 2017. First installed in March 2016, the AICD completion was adopted as part of the standard lower completion solution at East Belumut. To date additional wells have been completed with AICD completions in East and West Belumut fields, demonstrating significant increase in cumulative oil production, reduction in GOR of the AICD wells by 50%, and achieving 50% more oil production compared to offset ICD wells.
This paper describes a full field implementation for the application of AICDs in a super thin layer, oil reservoir offshore Malaysia. Nine new horizontal wells in two different fields were completed with AICDs to reduce the amount of water and gas production from these wells and to enhance the reserve recovery from the asset. The paper describes the workflow for establishing the suitability of candidates for the technology, the completion design process, and the enhanced production results of the program after 2 years production.
Cognitive Analytical System Based on Data-Driven Approach for Mature Reservoir Management
Authors: Gopa Konstantin , Yamov Sergey , Naugolnov Mihail , Perets Dmitrii , Simonov Maksim 
Published: October 2018

Abstract
The work tells about the experience of development and application of the cognitive analytical system prototype based on the approaches and principles of machine learning for the processing and analysis of statistical and dynamic data collected during the field exploitation. The developed system prototype implements an efficient and modern data-driven approach to field development management, and is an example of a paradigm shift in reservoir modeling.
In the process of creating a prototype of a cognitive analytical system for managing field development, both classical machine learning methods and modified capacitive-resistive models (CRM), INSIM models were used, as well as the approach of identifying a nonlinear dynamic system through recurrent neural networks (RNN). This system provides rapid analysis of a mature field development and to handle such management tasks as optimization of the water flooding, identification of inefficient injection and production, evaluation of the wells interaction, forecast of liquid and oil production at a given time horizon. The system consists of the models that have weak points and limitations, and is not an ultimate decision-making system, but should be used in conjunction with other tools for reservoir engineering. This tool will be an addition to the approaches of the "Digital Field", in which the field development is optimized not only based on the results of the classic physics models, but also on machine learning technologies applied to the entire development history.
Enabling Real-Time Asset Analytics for a Cloud-Based Fiber-Optic Data Management System
Authors: Yang Lei , Bale Derek S. , Yang Don , Raum Matthew , Bello Oladele , Failla Roberto , Lerohl David , Knowles David , Kwari Andy , Cannon Matt , Ye Shuzhen 
Published: October 2018

Abstract
The distributed nature of fiber-optic measurements such as distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and distributed strain sensing (DSS) enables nearly continuous monitoring of the downhole environment in both space and time. Though continuous monitoring opens the door to a rich new set of asset management applications, it comes with its own set of challenges in terms of data transmission, management, and security. Recently, cloud-based fiber-optic data management services have been successfully introduced to the oil and gas industry as an effective way to collect, transfer, store and display distributed measurement data from the downhole environment. To maximize the value of such cloud-based data management systems, and further improve the return on investment for asset managers, the large volume of distributed sensing data collected must be converted to value in a simple and easy-to-use form, depending on different applications. Traditionally, interpretation of distributed sensing data is a manual process conducted by engineers in a post-job workflow. This paper presents the successful integration of an analytics library into the cloud-based fiber-optic data management system. This integration enables real-time, and in some cases near real-time, asset decision making. The design of the analytics architecture is open to meet the wide range of application requirements by asset managers. A few application examples of the analytics integration will be presented using real-time data streamed directly from the field.
Automatic Geosteering of Wells
Authors: Stishenko Sergei , Petrakov Yuri , Sabirov Airat , Sobolev Alexey 
Published: October 2018

Summary
The issue of automatic transfer of well data was successfully resolved long time ago by means of WITS\WITSML protocols, and now automatic processing of the received data is on the agenda. The article describes the basics of automatic well geosteering, cross-check of the results of automatic geosteering performed by specialists, conceptualizes a new workflow and proposes scenarios for further development of the discipline.
Machine Learning Approach to Open Hole Interpretation and Static Modelling Applied to a Giant Field
Authors: Ismagilov Amir , Sudakov Vladislav , Nurgaliev Danis , Murtazin Timur , Usmanov Sergey , Nugumanova Natalya 
Published: October 2018

Abstract
This paper introduces description and results of creating a complex method for automatic interpretation of well-logging data and the further construction of the first approximation of the geological model. That procedure is aimed at mass re-interpretation of a large number of wells in terrigenous deposits of Tula and Bobrikovian horizons of Tatarstan. It was solved with the use of machine learning methods and artificial neural networks. We also proposed an improved solution to the problem of determining the reference value for normalization of neutron logging in the absence of reference horizon data. The priority task of well logs depth matching is solved by use of correlation coefficient, logistic regression and the idea that the expert has a different preference to different depth intervals in the investigated horizon. The next issue is the stratigraphic division which also was solved by logistic regression training as the most common tool in allocating one object from others and alternately defining each boundary of deposits. Solution for the problem of the reference maximum value of neutron logging in the absence of a reference Famennian stage was established by revealing the algorithmic relationship between the boundary values of the porosity for the reservoirs of the underlying carbonate reservoirs and the calculation of the porosity from the normalized neutron log. Further question of determining the lithology is solved with the use of two-layer perceptron and an expanded use of well log values, that is, a two-meter interval of curve values was chosen for interpreting each point in depth, which allowed to take into account the change zones of rocks and include shape of the curves in addition to their quantitative indications. Calculation of porosity and the oil saturation coefficient includes already existing dependencies for the processed objects of Tatarstan, therefore the accuracy of the calculation depends only on the quality of the determination of the reference values of radioactive and neutron logs, which was successfully carried out. The final goal in obtaining such a large volume of results of automated calculations for wells is the construction of a geological model, where a detailed correlation, grid construction and interpolation of petrophysical parameters on the grid are carried out.
Case Study of Digital Oilfield Implementation in the Southern North Sea
Authors: Elichev Vitaly , Muñoz Esteban 
Published: October 2018

Abstract
During times of volatile oil & gas prices, optimization of exploration, development and production costs per barrel of oil equivalent is the critical component for international upstream companies to ensure competitiveness and profitability.
Recently many companies have invested in data gathering systems, laboratory experiments and databases. Currently high amount of information is being gathered and stored in various databases and files. Therefore, a rapid, effective and smart information processing is crucial in decision making to maximize value of information and the asset performance. Considering field dynamic, processing the information real time generates value for maximizing key performance parameters and support on a day to day basis the complete production system.
The technology, which helps to process the information in operational conditions efficiently and improves operativity and quality of operational decisions is "digital oilfield". Wintershall is actively developing and implementing an own version of digital oilfield. Examples on a field case with focus on the dynamic model requirements in a tight oil field with strict flow assurance requirement, implementation of a ssuperposition principle and hybrid virtual flow meter are presented. This paper describes a case study of digital oilfield implementation in the Southern North Sea.
Optimizing Nanoparticle-Stabilized Emulsion Behavior in Porous Media Through Electrostatic Interactions
Authors: Daigle Hugh , Griffith Nicholas 
Published: October 2018

Abstract
We tested how different emulsion characteristics would affect transport through sandstone cores and recovery of residual oil. Our results show that the behavior of nanoparticle-stabilized emulsions flowing through porous media can be described in terms of filtration theory and electrostatic and van der Waals interactions. Residual oil recovery was enhanced by optimizing em—ulsion characteristics such as salinity, method of generation, and zeta potential. We emulsified widely available, low-cost natural gas liquids in brine using polyethylene glycol-coated silica nanoparticles. Emulsions were generated via sonication at varying salinities and zeta potentials for observations of emulsion characteristics. We conducted corefloods in Boise sandstone to assess the effects of different emulsion properties on residual oil recovery of heavy oils, effective permeability reduction capabilities (i.e. conformance control), and in-situ emulsion stability. Emulsions with high salinity content resulted in better in situ emulsion stability and up to 89% recovery of residual mineral oil at low injection rates. By increasing the salinity, the magnitude of the repulsive electrostatic force between emulsion droplets and grain surfaces is decreased, leading to increased droplet interception on grain surfaces. This results in more extensive droplet-pore throat blockage, redirecting the displacing fluid into less permeable zones. Increasing the magnitude of the droplet zeta potential of injected emulsions marginally increased in oil recovery, significantly reduced permeability, and increased in situ emulsion stability. The best residual oil recovery occurs when emulsion droplets can persist without coalescence under the pressures required to push them into small pore throats, while simultaneously moving through the larger pore throats rather than being mechanically or electrostatically retained. Proper emulsion flood design, therefore, must incorporate characterization of both the pore structure and the electrostatic properties of reservoir rocks and how these will interact with the emulsions.
Improving the Effectiveness of Diverters in Hydraulic Fracturing of the Wolfcamp Shale
Authors: Harpel Jennifer , Ramsey Lee , Wutherich Kevin 
Published: October 2018

Abstract
A project was initiated in the Wolfcamp shale to reduce the operational complexity and costs associated with hydraulic fracturing. The goal was to use dissolvable diverter to increase fracturing stage lengths while maintaining an average cluster spacing similar to the current completion design without affecting well productivity. To ensure maximum effectiveness, a unique methodology was employed that uses reservoir properties along the lateral to create stage specific diverter strategies.
The methodology used to design the diverter strategies begins with understanding well heterogeneity along the lateral. Estimations of minimum in situ stress at each cluster were combined with estimates of pressure increases caused by stress shadow both from previous stages and between treatment clusters to determine fracture breakdown pressures along the lateral. This data was used to selectively segregate the clusters into those that would be treated before diversion and those that would be treated after diversion. Additionally, calculations including hoop stresses and perforation friction were used to ensure pumping pressures remained in a range that increased the probability that clusters designed to take fluid after diverter were not prematurely broken down during the initial pumping treatment.
This approach of engineered diversion was applied to three wells located in the Wolfcamp shale of the Midland Basin. The completion incorporated a designed stage length that averaged 315 ft with nine perforation clusters per stage using a single diversion drop. Typical well designs in this field contain stages that are 175 ft in length with five perforation clusters. Thus, this revised design constituted an eighty percent increase in stage length over conventional stage designs.
The goal of the treatment was to increase stage length without affecting production. During the treatment of the new engineered diverter stages, there was clear indication that after the first portion of the fracture was completed and diverter had seated on the perforations, the fluid was effectively diverted to virgin rock. This is a positive indication that the stage-specific diverter design was effective. Additionally, when comparing production between the three wells in this study and conventionally stimulated offset wells, there was no appreciable difference in production.
This case study represents one of the earliest applications of a fully engineered diversion strategy and will describe how lessons learned during this application can be applied to further improve economics and effectiveness of diverters in horizontal shale plays.
Application of Machine Learning for Oilfield Data Quality Improvement
Authors: Andrianova Alla , Simonov Maxim , Perets Dmitry , Margarit Andrey , Serebryakova Darya , Bogdanov Yuriy , Budennyy Semen , Volkov Nikita , Tsanda Artem , Bukharev Alexander 
Published: October 2018

Abstract
The paper describes the principal possibility of using machine learning methods for verifying and restoring the quality of oilfield measurements. Basic methods for screening incorrect values have been given and approaches for solving three problems have been recommended:
Correctness analysis of well logging data Quality control of physical and chemical fluid properties (PVT-studies) Separation between the base production and effect from well interventions (WI) to predict the performance of hydraulic fracturing (frac).
The main deliverable is a set of algorithms based on machine learning methods, which allows to automatically process large volumes of field data. A number of approaches is proposed, including using modern methods of machine learning, to restore the missing values and the quality of algorithms operation.
Innovative Strategy Decision and Portfolio Assets Analysis Instrument – Permanent Model of On-Line Reserves and Resources Monitoring
Authors: Zaharova Oksana , Teplouhova Irina , Islamuratov Marat , Kisurina Anastasiya , Panfilova Elena , Stepanova Valeriya , Ershov Aleksandr 
Published: October 2018

Abstract
The effectiveness of reserves and resources management depends on the full performance about resource base of the Company, its development history, current status and future prospects. Existing methods of data evaluation and analytics are not interconnected between each other and provided in the different systems by the various specialist's background.
The volume of information is increasing yearly despite production conditions are getting complicated. Extremal climate conditions, remote territories, unconventional reserves are usual circumstances of actual oil and gas industry. The only method to save the cost production level is enhancing business process efficiency.
Historically the Oil and Gas industry has a huge risk of failure – dry well or incorrect development plan may lead to great losses and jeopardize company's sustainability. For the very reason it is very important to increase effectiveness and rationality of the Resource base Management using disparate data source.
In response to the complex needs of the oil and gas industry, it became necessary to use a single software solution for strategic decision making based on resource base analysis. Russian and foreign markets analysis showed the lack of software that is fully complies with the business needs. This fact encourage Gazprom Neft to develop its own IT solution for managing the company's reserves and resources: "Constantly operating reserves and resources model (PDMZiR)". This is a unique tool that aggregate huge amount of information from different data sources and modern technics for Resource base Management in one platform. PDMZiR can significantly increase the quality of resource base estimations for any Oil and Gas company, improve assets potential, identify and prevent any significant risk, justify recommendations for field development.
Determination of Lithologic Difference at the Bottom of Wells Using Cognitive Technologies
Authors: Simon Igor , Koryabkin Vitaly , Semenikhin Artyom , Gruzdev Arseniy 
Published: October 2018

Summary
In this paper, we present a methodology for determining lithological difference at the bottom of the well during drilling operations. Our approach is based on the analysis of mechanical parameters of drilling. These parameters are receiving as real-time time-series data. The central part of the methodology is a model based on the machine learning approach. Our model and the whole methodology can be applied to real drilling cases. The set of parameters that are required for the methodology can be collected from the typical mud logging station.
The main use case for the methodology is an optimization of the geosteering process. The most modern geosteering approaches are based on the LWD data. It is the main restriction of common approaches for the adjustment of the direction of drilling. The problem is that the LWD sensors are placed for a few decimals meters before the bit in a typical Bottom Hole Assembly (BHA) design. As a result, these a few tens of meters are drilling in a "blind window".
The methodology is illustrated on the historical data of drilling of the Novoportovskoe oilfield. At the current stage, the results of the testing show that suggested methodology can correctly classify two out of three cases of changes of lithotypes while drilling.