bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lithofacies Classification and Distribution for Heterogeneous Channelized System Based on Neural Network Process: Case Study from Middle East Carbonate Reservoir
Authors: Al-Ali Ali , Stephen Karl , Shams Asghar 
Published: October 2020

Abstract
Characterization and prediction of channelized systems is significant for the exploration of hydrocarbon and reliable estimation of reservoir production. However, the precise prediction of the spatial distribution of its heterogeneity, is a challenging task in the inter-well region because of irregular channel patterns. With the accessibility of 3D or 4D seismic data, there has been great interest in using machine learning models for seismic reservoir characterization such as facies classification and determining the nonlinear relationship between reservoir properties and seismic attributes. This paper will mainly focus on the application of neural networks algorithm to predict the presence of different seismic facies in heterogeneous channelized reservoir.
Supervised artificial neural natural (ANN) algorithms is a robust classification tool that integrates multiple seismic attributes into a number of classes to recognize seismic facies. In the first scenario, based on the available data, core and logging data analysis is conducted on main carbonate reservoir zone to recognize the development phases of the channel. In the second scenario, for a target carbonate tidal channel facies, we considered an approaches that used to classify and predict seismic lithology. We build and train Probabilistic Neural Network (PNN) to predict quantitatively the facies of carbonate channelized zone and the passageways laterally. We further quantified the distribution of tidal channel characterization through an acoustic impedance volume and seismically derived porosity volume. The representativeness of the channelized facies was analysed through blind tests using well logs as well as core data.
The most significant findings of our work that the 3-D seismic facies classification maps generated from PNN using appropriate seismic attributes provides two different facies which identified in the channelized systems. These facies are channels and restricted lagoonal related to depositional phases and flooding surfaces intervals. Our work clearly demonstrates that a multi-step workflow is important for classification and characterization of heterogeneous channelized systems facies. The value of this study is also to provide an interpretation of complex carbonate tidal channels, with quantification of the impact on dynamic behavior in the main oil-bearing reservoir of the case study.
Application of Machine Learning in Offset Well Planning
Authors: Adlakha Khushal , Lyngvi Eirik , Larssen Nicholas Mowatt , Tvedt Magnus 
Published: October 2020

Abstract
Planning a well for safer and faster operations requires complex and experience-based decision making. In this aspect of the oil and gas industry, the main source of information is the limited amount of offset well data. The offset well data is spread across different domains which makes it more difficult to analyze in the stipulated period of time and in turn the quality of a planned well is always sub-optimum. The main objective of this study is to make use of machine learning and deep learning algorithms to increase the scope of offset well planning by analyzing a large number of wells efficiently. This approach represents an alternative workflow for offset well planning with a focus on data-driven KPI's (Key Performance Indicators). The significant KPI's that can be tracked is increased use of reference wells with a drastic reduction in time required for waiting and searching for data.
The area of this study is the Norwegian Continental Shelf (NCS) encompassing wells in the North Sea, the Norwegian Sea, and the Barents Sea. The dataset consists of around 4500 wells with high dimensionality features based on stratigraphy data and well path data (eg. top and bottom depth, thickness, azimuth, inclination etc.) for each well. A new methodology is discussed in this study that includes the use of deep learning algorithms i.e. Convolutional Neural Networks (CNN) and non-linear algorithm for dimensionality reduction (t-Distributed Stochastic Neighbouring Entities and Uniform Manifold Approximation And Projection) on this high dimensional dataset.
The results of this study show that the reduced dimension data obtained from the above methodology is able to extract high-level features from the wells based on the input data/features. Obtained embeddings show the relative distance between the wells which can then be used to find how similar the wells are with respect to each other.
The value of the results obtained from this study can potentially increase the horizon for offset well planning by incorporating not just the nearby wells but all the wells from the corporate databases. This, in turn, tracks key KPI's and saves time by providing automated answers to the engineers.
Simulation Study for Field Implementation of WAG as Initial Drainage Strategy
Authors: Larsen Alex , Førland Tormod , Øian Erlend , Olav Jarlsby Diderik 
Published: October 2020

Abstract
Water Alternating Gas (WAG) injection is normally implemented after water injection to increase microscopic and macroscopic sweep efficiency. The microscopic sweep is enhanced by trapping of gas, three-phase flow effects including lower residual oil saturation, and possible component transfer between gas and oil. The macroscopic WAG-effect is mostly associated with recovery of attic oil or other pockets of oil not yet swept by water.
This study presents the results from simulation of a WAG process for an offshore turbidite reservoir with undersaturated oil. The aim of the work is to qualify the increased drainage potential from WAG injection by understanding the reservoir response, which includes cycling of saturations in two- and three-phase flow as well as changes in fluid properties due to interaction between gas and oil.
A hierarchical workflow has been followed in order to conduct a systematic and comprehensive analysis of the WAG process. This includes the use of simulation models from full-field scale models (FFM), via box-models, to simpler 1D models. The full-field model, with appropriate well constraints reflecting top-side requirements, is the basis for estimating an extra WAG potential of about 3% of additional OIIP recovered compared to water injection. WAG injection is further modelled on the field scale without any topside constraints to get the free reservoir response of WAG, and to investigate the impact of relative permeability and fluid properties on production curves as well as comparison to water injection. Furthermore, grid effects, two- and three-phase flow properties and the effect of oil swelling is studied in smaller box models and 1D models.
The results from the workflow qualify the enhanced oil recovery from WAG compared to a water injection scheme. Approximately 1/2 of the increase in recovery factor is attributed to gas-oil relative permeability, about 1/5 is attributed to a change in fluid properties from oil swelling, and 1/3 is due to increased well-lift.
A Drilling Company's Perspective on Non-Productive Time NPT Due to Well Stability Issues
Authors: Krygier Natalia , Solarin Adebowale , Orozova-Bekkevold Ivanka 
Published: October 2020

Abstract
Non-Productive Time (NPT) is a key measure of cost-effective and successful drilling operations. NPT may be caused by various reasons, such as unpredictable weather or technical issues. Some of those cannot be reduced or controlled (weather), while others can be counteracted by proper preparation and careful planning. Industry experience shows that time lost due to well stability issues accounts, on average, for 10% of all NPT. This can be eventually reduced by appropriate measures and/or procedures.
Offshore drilling operators are collecting and analyzing large amounts of data but it is very rare to perform analysis on unstructured drilling reports like the ones produced and possessed by drilling contractors. This project's aim was to analyze the NPT of drilling operations from the perspective of a contractor. Data from 93 wells drilled by Maersk Drilling from 2006 to 2019 in the North Sea was used to classify, compare and analyze the causes of NPT, as registered in the daily reports, with a focus on well stability issues by the means of text mining and statistical tools. The analysis had shown that the average cost of wellbore stability NPT was in the order of 2,000,000 USD per well and losses were the most common cause of wellbore instability among the wells in the database.
This project was, to the best of our knowledge, the first attempt to extract and analyze information from unstructured daily operation reports collected by a drilling company. The analysis had shown that the contractor's reports very often do not contain the same degree of detail on the causes of NPT in general, and wellbore stability issues in particular, as the ones produced by the operator. The results of our analysis can be used as a framework for improvement of current practices for registration and categorization of NPT causes in the drilling contractor's reports. In future, these reports should be structured in a way that allows to record both the symptoms of wellbore stability issues as well as the issues themselves. A well-structured report will not only allow to analyze easier historical data but also may be used to facilitate the automatization of the drilling process. Based on experiences from previous wells, specific NPT symptoms and scenarios could be identified and compared with current situations therefore allowing to promptly forecast and counteract possible issues.
The ultimate goal of such structured reports is to create an efficient database and provide guidelines to the drilling crew on how to recognize and minimize NPT related to wellbore stability problem. Suc h database can help make drilling more predictable by providing field-specific wellbore related problem data, which can be used to ensure the planning of effective mitigating measures. The method used for this analysis can also be extended to other problematic areas.
Hybrid Approach for Drilling Automation
Authors: Aghito Manuel , Bjørkevoll Knut Steinar 
Published: October 2020

Abstract

Objectives/Scope
The objective of this work is to present a first step towards a hybrid approach between machine learning (ML) and physics-based modelling to provide decision support for drilling problems. The motivation for developing a hybrid approach is to obtain methods that are more reliable and easier to automate than physics-based models, while still have enough accuracy and predictivity. In this first step, we replicate the performance for predicting downhole pressure in a well of a high-fidelity simulator based upon physical principles by using ML methods. In addition, we also suggest a future roadmap.

Methods, Procedures, Process
A high-fidelity physics-based model for drilling and well control operations is used to generate vast amounts of data for two cases, drilling with not major events, and drilling into an over-pressured reservoir. Key simulation input parameters and assumptions are varied to create realistic scenarios. We replicate the high-fidelity simulator downhole pressure predictions by two supervised machine learning algorithms. Random forest (RF) and recurrent neural network (RNN). The hybrid approach is flexible and is also employed for kick detection and estimation of the mass of the influx. After using unaltered data from the high-fidelity simulator, we also demonstrate the ML methods on corrupted data with synthetic noise.

Results, Observations, Conclusions
RF and RNN obtained very high accuracy, predicting bottom hole pressure with small error margin. Good results were also obtained for the kick estimation and kick detection cases. Tested on corrupted data, RF trained with noise performed significantly better compared to RF trained without noise, at the cost of a slight reduction in accuracy in the error free scenario. Initials tests on real data are ongoing and further work is needed. Hybrid methods have the potential of performing well with noisy environments and are valuable tool to be used in drilling automation.

Novel/Additive Information
Combining highly advanced dynamic models for drilling and well control with modern ML methods has not been done earlier to the best knowledge of the authors. Demonstrating this on real data will be valuable because data-driven and physics-based approaches used separately are considered inadequate for future automated drilling concepts.
Implementation of Ensemble-Based Reservoir Modelling on the Ærfugl Field
Authors: Tveteraas Ørjan , Vey Geraldine , Hjellbakk Andor , Wojnar Konrad 
Published: October 2020

Abstract
An ensemble-based modelling method has been implemented and utilized in reservoir management of the Ærfugl field. Ærfugl is a 60-km long, 3-km wide gas condensate accumulation in a Cretaceous deep marine deposited reservoir with high net-to-gross. The field has a 6-year long production history from one test producer. A key objective of the modelling effort was to take an uncertainty-centric approach that could integrate all available data. The methodology of this approach is presented, and its benefits and challenges are discussed. The quality of the automated history matching is shown, and the resulting uncertainty ranges are discussed. Practical use of the ensemble for forecasting and decision making is also presented.
A set of 100 reservoir models was created with Monte Carlo sampling. All uncertain parameters that were deemed important were varied, such as geological input surfaces, geostatistical parameters, petrophysical parameters, relative permeability curves, etc. All models were then conditioned to dynamic pressure and production data using an ensemble Kalman based method. The updates made by data conditioning apply to all uncertain parameters as defined in the generation of the initial ensemble, including facies and structural modelling.
The data conditioning arguably produced an adequate history match for the entire model ensemble. The posterior uncertainty range (after integrating dynamic data) remained high for most variables, but uncertainty related to in-place volumes was shown to be highly constrained by the dynamic data. Production forecasts were generated, and probabilistic production profiles were extracted. Four deterministic cases that were representative of the range of important parameters were extracted from the ensemble for use in certain applications where probabilistic modelling is not appropriate, or time constraints impose limitations. The model ensemble and associated production profiles were used as input to project economics, for a value-of-information study, and for well planning.
In addition to going though the work process and results, this paper aims to shed light on the method's practical effectiveness, the potential impact on the way we do reservoir management, and to highlight some specific issues that arise in practical application. The overall set-up is effective at eliminating waste by enabling rapid model updates as new data comes in and making use of existing data that would otherwise be under-utilized.
Autoviscosity - A Vital Step Towards Automation of the Drilling Fluid Process on Drilling Rigs
Authors: Bjørkevoll Knut Steinar , Aghito Manuel , Lund Bjørnar , Blom-Jensen Bjarne , Torpe Geir , Brevik Jan Ove , Fjogstad Arild 
Published: October 2020

Abstract

Objectives/Scope
The paper describes what steps have been taken towards improved monitoring and control of the offshore drilling fluid process. These steps include the following:

Methods, Procedures, Process
The predictive viscosity model tracks the active fluids, calculates additives concentrations in the flow loop, analyses sensor data, calibrates viscosity response to additives, and estimates what is needed to obtain the set target rheology. Laboratory and full-scale tests both onshore and offshore have been performed using the automated rheology and density sensor. To prepare for automating the drilling fluid conditioning process, the predictive rheology model was integrated with the drilling fluid control system and sensor data. Additionally, integration with database and cloud service was implemented to facilitate transfer of data from the rig to the onshore.

Results, Observations, Conclusions
During laboratory scale experiments, the predictive rheology model represented topology flexibly and was configured to represent the active flow loops in laboratory scale experiments, full scale onshore test, and offshore test with two sensors in the loop. The data processing algorithm with data quality assessment and model calibration was tested with onshore full-scale data. Many challenges were addressed and solved by cooperating partners in the integrated fluid laboratory, which included two sensors, the predictive rheology model, drilling fluid control system and a flow loop with multiple active tanks and mixing lines with dosing units for adding liquids and powders. The first response tests for the calculated additive rate set-point were successfully performed. Full-scale onshore tests were completed while adding different additives and recording the rheology data. The calibrated predictive model showed good correlation with the recorded data. Temperature effects were also considered and modelled accurately. Two sensors were installed offshore, recording data during regular drilling operations.

Novel/Additive Information
New elements required to automate the drilling fluid treatment process have been implemented and tested, including a predictive rheology model and data integration between different vendor's software/control systems and equipment.
Digital Twins for Well Planning and Bit Dull Grade Prediction
Authors: Gharib Shirangi Mehrdad , Furlong Edward , Sims Kelly Scott 
Published: October 2020

Abstract
In this work, we develop and apply an offset well data analysis framework to generate a digital twin that is representative of bit state. We also strive to produce performance maps for well planning. Our workflow involves three major elements: 1) offset well data analysis to generate detailed depth-based and time-based statistics 2) computation of wear on bit and efficient weight-on-bit (WOB) versus depth for all the runs 3) automated machine learning to generate an accurate predictive model for bit dull grade to deploy for real-time operations. We define and calculate the efficient WOB as the minimum WOB needed to fully engage all cutters on the bit and maximize depth of cut. The efficient WOB changes with both bit state and formation strength, so an indication of bit wear can be used to predict formation strength. Deployment of the bit dull grade predictor enables the drilling crew to monitor the bit state while drilling and decide whether a reduction in ROP is due to a worn bit or other factors. Application to field cases from US Land yielded promising results. After computing the bit wear, performance maps for different bit states (e.g., sharp vs worn bits) are created. These performance maps help identify the optimal drilling parameters in well planning.
Non-Newtonian Fluid Flow Measurement in Open Venturi Channel Using Shallow Neural Network Time Series and Non-Contact Level Measurement Radar Sensors
Authors: Noori N. S. , Waag T. I. , Viumdal H. , Sharma R. , Jondahl M. H. , Jinasena A. 
Published: October 2020

Abstract
Drilling is a costly operation, especially offshore, and disturbances caused by drilling fluid influxes (kicks or loss) pose persistent challenges and operational costs during drilling process. To minimize non-productive time induced by kick/loss of circulation, drilling systems incorporate methods monitor and measure inflow and outflow of drilling fluid for early detection of kicks/losses. Coriolis flowmeters are proven to be very accurate even under difficult mud properties. However, installation and maintenance and cost of these instruments are complex and high. Thus, new exploits of technological advances such as high-performance computations (HPC), artificial intelligence (AI), low-cost sensor technologies could enable real-time data acquisition and analysis for early kick/loss detection to improve the safety and performance of oil and gas drilling process. In this study, we focus on the outflow measurement using a combination of sensors (i.e. radar and ultrasonic sensors) to remotely obtain the flowrate in an open channel with Venturi constriction.
Optimizing Well Economic Limits Using Ensemble-Based Optimization on Olympus Field
Authors: Chang Yuqing , Nffivdal Geir , Lorentzen Rolf Johan 
Published: October 2020

Abstract
The Olympus field is an optimization case prepared by TNO to test different optimization methods and workflows for field development. The authors have joined the Olympus optimization challenges by solving various optimization problems for field development. Here, we consider the well control problem from a different perspective by using well economic limits as parameters to optimize the objective function - the Net Present Value (NPV).
The Ensemble-based Optimization (EnOpt) method applied to this problem has been shown to be simple and efficient to handle geological uncertainty in many literatures. In this work, the performance of EnOpt is illustrated on the Olympus benchmark case. The economic limit determines when a well will be shutin with respect to the water-cut, which directly affects the calculation of NPV. After optimizing the economic limits, we see that the producers are not necessarily following the fixed value of economic limits to give the best production benefit in terms of NPV. Given a well control scheme in the field, the NPV can be further improved by optimizing the economic limits of each producer.
Optimizing the economic limits is a novel aspect for Olympus case, as this was not within the original scope of the Olympus optimization challenge. This work also shows that the NPV is further improved compared to the values obtained by the authors during the Olympus optimization challenge by utilizing optimized well economic limits.