bluuebunny/crossref_metadata_2025_split huggingface dataset:
Increasing Recovery and Lowering CO2 Intensity Through Use of Autonomous Inflow Control – A Case Study from the Norwegian Continental Shelf
Authors: Halvorsen Martin , Martiniussen Ingvild Berg , Nilsen Knut Arild , Hånes Margrete , Panini Filippo , Amundsen Lene , Elseth Geir 
Published: September 2024

Abstract
The aim of this paper is to demonstrate how the adoption of autonomous inflow control (AICD) can lower water production and CO2 intensity in a field situated on the Norwegian continental shelf. The study outlines the process starting from device qualification and testing in a flow loop, followed by evaluation through modelling and reservoir simulations, then well planning to optimize AICD implementation, and finally the measurement of the effect in several wells.
Breidablikk is an oilfield located on the Norwegian continental shelf that commenced oil production in October 2023. The oil reserves are at a depth of about 1750 meters, forming a thin layer that sits atop a strong water aquifer. Prior to production, a range of AICD solutions aimed at water reduction were evaluated, including qualification, and testing of the devices in a flow loop to obtain their flow performance data. This data was subsequently used as input in multiple reservoir simulations to determine the optimal distribution of AICDs in the wells. The wells are closely monitored to confirm that the expected AICD behavior has been achieved.
The Breidablikk wells consist of long horizontal sections, both single and multilaterals, with lengths of up to 1600 meters. They are equipped with screens, various inflow control devices, blank sections, and swell packers. Following implementation and production start-up, measured flow rates and pressures are compared against the expected AICD performance. The lower completion design is optimized using a steady-state well simulator and the use of a dynamic reservoir simulator to provide expected production profiles. During the stepwise clean-up of the wells and branches, differential pressure and oil rates are used to measure well clean-up and AICD efficiency. The pressure drop across the AICDs is determined based on down hole pressure, plotted versus initial well test rates, and is consistent with laboratory tests under similar conditions. Chemical tracers have also been used to verify clean-up during flow performance evaluation. Based on the successful implementation, there is a good basis for planning and optimizing use of AICDs in future wells at Breidablikk.
The reservoir drainage strategy for Breidablikk depends upon implementing artificial lift and utilizing the well designs mentioned earlier. Production of the wells at minimal gas lift rates, in combination with on/off well production, allows for higher NPV and recovery to be attained throughout the anticipated field lifetime. To ensure a successful implementation, step rate tests are performed to affirm the AICD flow characteristics determined through lab testing and simulations.
Towards Robust and Automated Contamination Estimation During Downhole Fluid Sampling
Authors: Berkane M. , Kristensen M. , Ali A. M. , Al-Hamad M. , Albuainain F. 
Published: September 2024

Abstract
Accurate, robust, and real-time quantification of oil-based mud (OBM) filtrate contamination using data from downhole fluid analysis (DFA) sensors remains a challenging problem, especially under difficult sampling conditions and for advanced sampling tools with complex inflow geometries and active guarding of filtrate flows. Such tools and conditions lead to contamination cleanup behaviors that do not necessarily follow the simple power-law models that are commonly used in contamination monitoring algorithms. Moreover, the need for improved efficiency calls for more automation in sampling operations, especially in high-activity regions with many wells and sampling stations. Achieving such automation within existing contamination monitoring workflows has been proven to be challenging.
In this paper, we deploy a new method for contamination monitoring, based on the inversion of DFA data using a full 3D numerical flow model of the contamination cleanup process. This flow model predicts the evolution of filtrate contamination as a function of time and pumped volume and can thus be used to forward-model the DFA sensor responses. Sensor data, such as optical density, mass density, and gas-oil ratio, are then inverted in real time to provide contamination predictions. Real-time computation is enabled through fast, high-fidelity proxy models for the cleanup process. The proxy models are trained on and validated against a library of 300,000 precomputed full-scale numerical simulations covering a wide range of hardware configurations and sampling environments.
We applied the new method on 14 field sampling stations of oil sampling in OBM conditions, with four different types of sampling inlets: single probe, radial probe, dual packer, and focused radial probe. The data sets also included challenging stations with cleanup conducted over multiple 4-hour sessions with possible reinvasion of mud between sessions. We found that contamination predictions with the new method are in good agreement with results from laboratory analysis.
Compared to the previous algorithms, the new contamination monitoring algorithm is applicable for all types of sampling hardware, a wider set of operating conditions, and over the full history of a sampling station (i.e., from formation fluid breakthrough to a clean sample). This latter attribute enables robust early-time predictions of the pumping time remaining to reach a clean sample. Finally, the new method is independent from manual user intervention and thus lends itself well to workflow automation.
Comparing Open-Source Drill String Models: Establishing Test Cases and Methods
Authors: Yoon D. , Hamidov E. , Endres L. , Gildin E. , Pastusek P. , Shor R. , Li X. 
Published: September 2024

Abstract
This paper presents a foundational guide for comparing drilling dynamics models, emphasizing the establishment of methodologies rather than just results. We assess two open-source soft-string dynamic models: the distributed, torsional model by Aarsnes and Shor, and the lumped-mass, coupled axial-torsional model by Dixit et al. Both models are evaluated through six progressively complex test cases designed to enhance methodology development for future model comparisons. Despite their different assumptions and mathematical foundations, both models demonstrated similar responses in drill string vibrations and stick-slip behaviors, with minor differences attributed to the use of distinct friction models (Coulomb versus Stribeck). The study also illustrates the benefits of open-source software in drilling dynamics, such as ease of modification and problem-solving speed, highlighting its potential for improving productivity and insights when combined with collaborative efforts from various industries and academic entities. This work lays the groundwork for further research and robust comparisons of drilling dynamics models, enhancing confidence in both open and closed-source models.
Physics Augmented Machine Learning Models for Determining Gas Solubility in Formation Brines for CCS and Gas Processing Applications
Authors: Ratnakar R. R. , Chaubey V. , Gupta S. S. , Hackbarth J. , Rui Z. , Dindoruk B. 
Published: September 2024

Abstract
Gas solubility in brine is crucial input for engineering design of various chemical/petroleum processes such as oil recovery, CO2 sequestration in saline aquifers and water bearing formations, separation and utilization, corrosion in wellbores/facility/pipelines with acidic gases, and gas processing. However, with limited experimental data, intrinsic trends of gas solubility under varying conditions (pressure/ temperature/brine composition) are difficult to obtain. This study uses machine-learning-based approach to explore those intrinsic trends while identifying the contributing elements correlated with fluid properties.
Gas solubility in brine depends on various thermodynamic properties of the components, pressure, temperature, and salinity. To explore effect of these factors, we employ various Machine learning (ML) tools: decision tree (DT), random forest (RF) and artificial neural network (ANN) techniques due to their stability and convergence characteristics coherent with the data utilized and develop a framework to determine solubility of a gas in brine solutions. While most existing literature are limited to very few salts (NaCl/KCl/CaCl2), this work captures combinations of various common salts (chlorides, carbonates/bicarbonates, and sulphates) as they are seen in real formation brines and water utilities.
The prediction from ML models were validated against the available experimental data that were not used in training. The main results are as follows:
Validation processes indicated that ML models predict the experimental trends accurately, within the relative error of 1% for gas-water systems and 3% for complex gas-brine systems. Various input features based on the thermodynamic and physical properties of gases and ions (cations and anions) were considered and main contributing features were identified. Most importantly, the framework is general, fast, convenient and can easily be extended for different gas species including greenhouse or hydrocarbon gases, as well as for variety of salts. Additionally, it can fill the gaps in experimental data for the gas-brine systems, and extrapolate to elevated pressure and temperature conditions.
While ML-based approach to estimate gas solubility in brines have been developed in the literature, they are very restrictive in terms of their broadness/applicability and valid for specific gases such as CO2 as well as few salts (NaCl/KCl/CaCl2). Here, the ML-based framework covers a wide range of salts and gases, and additionally, the current framework can easily be extended to other systems.
Phase Behavior of Hydrocarbons in Shale Nanomatrix-Fracture System: Experiment and Simulation
Authors: Dong Xiaohu , Xiao Zhan , Guo Ziyang , Liu Huiqing , Lyu Xiaocong , Chen Zhangxin 
Published: September 2024

Abstract
Currently, the methods of nanofluidic chip and molecular dynamics simulation have been widely applied to characterize the effect of nanoscale confinement on the fluid phase behavior in shale rocks. However, most of the published literatures just concentrate on the pure nanoscale pores. Actually, in shale rocks, the phase transition phenomenon of fluids is happened in a nanomatrix–fracture system, which highly differs from the pure nanoscale pores.
In this work, we combine the methods of experiment and simulation to address the phase behavior of hydrocarbons in a nanomatrix–fracture system, which can effectively represent the actual pore space of fluids in shale. A new experimental device for the fluid phase behavior is firstly developed in this study, which is based on the conventional PVT test equipment. But for this newly-proposed device, the test cell is separated into two connected spaces. During experiment, one of them is filled with nanoporous material to represent the shale nano-matrix, and the other one is used to simulate the fracture system. Then, by using this device, through a step-wise reduction on the test cell volume, the bubble point pressure of a hydrocarbon mixture (C1/C8) is tested. The applied nanoporous materials in this study include MCM-41 (pore size: 4 nm) and SBA-15 (pore size: 2.5 nm). Through a comparison, the effect of nanopore size is analyze. Thereafter, the obtained experimental data are compared against the simulation results of our previous proposed mathematical model to discuss the effect of fracture system. Simultaneously, a set of Grand Canonical Monte Carlo (GCMC) simulation runs are also performed for the microscopic mechanisms for the nanoconfinement effect on fluid phase behavior.
The obtained bubble point pressures of C1/C8 mixture in the SBA-15 and MCM-41 porous systems are 4.65 MPa and 4.80 MPa respectively. They are lower than the that of the pure bulk fluids (5.07 MPa). It can be found that with the nanopore size reduces, the deviation is obviously increased. Then, the experimental data is compared with the calculation results of our mathematical model (4.22 MPa and 4.37 MPa), it is found that without the consideration of fracture system, the bubble point pressure of hydrocarbons can be underestimated. Furthermore, based on the GCMC simulation results, it is found that the wettability characteristics of shale rock can significantly impact the phase behavior of hydrocarbons, while the pore size distribution in shale typically controls fluid phase transitions during production. This study provides a novel experimental method to characterize the fluid phase behavior in nanoporous shale rocks. Some important new insights are obtained to understand the complicated phase transition phenomenon in shale reservoirs.
Wellbore Stability Uncertainty Quantification Using a Bayesian Machine Learning Framework
Authors: Ray Peng L. , Tipton John R. , Cheng S. , Francom Devin 
Published: September 2024

Abstract
One of the major challenges in wellbore stability analysis is to quantify the uncertainty of the mud weight window and provide probabilities of breakouts and drilling induced tensile fractures for a given mud weight. Modeling the safe drilling window requires many geomechanics inputs, such as: rock strength, elastic properties, in-situ stresses, pore pressure, etc. Most of these inputs are calculated using empirical correlations based on limited test data; or calculated using simple elastic equations and calibrated with limited data points. Uncertainties in test and calibration data combined with uncertainties in correlations make it exceedingly difficult to correctly assess the uncertainty of the resulting safe drilling window. An accurate quantification of measurement and other input uncertainties and correct propagation of uncertainty throughout the modeling process can improve decision quality and wellbore instability related non-productive time and drilling costs.
Geomechanics modeling of a complex physical system requires a solid understanding of fundamental physics and domain expertise. However, current uncertainty methods often lack a mathematically rigorous framework that can integrate both physics and domain expertise into the modeling process.
To address the challenges mentioned above, a Bayesian machine learning framework has been adopted as the foundation of our physics-based uncertainty workflow, which allows to incorporate prior information from domain knowledge and fundamental physics, to integrate collected lab and field measurement data into the modeling process, and to improve the model using an iterative workflow.
The resulting workflow can account for the complex interrelationships and interactions between different components in the geomechanics system; specifically, the capability to model and calibrate the uncertainties for several essential components underlying the one-dimensional wellbore stability model. These components include geomechanical core tests and stress measurements (such as Leak-off Test (LOT), Extended Leak-off Test (XLOT), Diagnostic Fracture Injection Tests (DFIT), etc.), as well as drilling performance (such as mud weight used, appearance of breakout and drilling induced tensile fractures, etc.).
This workflow provides a systematic and consistent framework for evaluating uncertainty in building a 1D Mechanical Earth Model (1DMEM) which is a crucial step in ensuring a safe and efficient drilling process. We will demonstrate this uncertainty quantification workflow and its application for estimating the safe drilling windows with a case study.
Best Practices of Inline Tests and Deliverability Assessment in the Canadian Low Permeability Duvernay for Improved Design of Completion and Characterization of the Reservoir
Authors: Virues C. , Robertson A. 
Published: September 2024

AbstractIn March 2021, the Alberta Energy Regulator (AER) introduced a new directive on pressure and deliverability testing, providing updated guidelines for inline testing to match industry practices. The AER now receives from oil and gas operators flowback data from inline tests and absolute open flow (AOF) interpretations. After analyzing this data, the AER has developed best practices. Best practices resulting from this study are contrasted to published studies of the Duvernay Formation.Currently, the AER cannot obtain fracture geometry and other fracture parameters. With the addition of pressure data, this study will aid the AER in obtaining fracture geometry, such as half-length, and fracture parameters, such as stimulated reservoir volume (SRV), permeability and productivity parameters. The new interpretation will aid and enhance reserves assessment and improve our understanding of inter-wellbore communication. New descriptions using mathematical (analytical/numerical) methods allow improved design of completions and enhanced characterization of the reservoir. This technical paper provides fundamental equations along with inline test and AOF interpretation techniques, which can be easily replicated by subject matter experts.Eighty unconventional flowback submissions were analyzed and displayed for non-vertical and non-deviated wells in the Duvernay. Productivity indices were calculated using flowback information and the AOF-based C value-related index of productivity (PI). Fracture geometry, SRV permeability, and PI estimates all significantly affect characterization of the reservoir. Currently, the AER only uses production rates in its flagship report AEO. This study will enhance the Duvernay reserves evaluation by using rate transient analysis (RTA) along with pressure parameters, such as tubing/casing pressure. Results from the study will affect what industry could do not only in the Duvernay but also in similar low-permeability resource plays in the United States of America and elsewhere. Production and pressure data aids in the interpretation of flow regimes to determine the optimal separation between wells and designs of completions.Flowback information has been used before. However, our work is novel in how it applies to the Duvernay Formation, which has been target of many companies in Alberta, and the implications on reserves evaluation, completion design, and characterization of the reservoir.
Embed-to-Control-Based Deep-Learning Surrogate for Robust Nonlinearly Constrained Life-Cycle Production Optimization: A Realistic Deepwater Application
Authors: Nguyen Quang Minh , Onur Mustafa , Alpak Faruk Omer 
Published: September 2024

SummaryThis paper presents a realistic deepwater application of a deep-learning-based reservoir surrogate model in forecasting reservoir performance (via the prediction of state variables and well outputs) and nonlinearly constrained production optimization under geological uncertainty by utilizing an efficient gradient-based method. The reservoir surrogate model is based on the multi-model Embed-to-control Observe (E2CO) architecture, consisting of four blocks of neural networks: encoder, transition, transition output, and decoder. In this work, the surrogate model is coupled with the powerful stochastic-gradient-based line-search sequential quadratic programming (LS-SQP) workflow to handle robust production optimization in the presence of nonlinear state constraints. Five realizations of the synthetic realistic Reservoir X model with different channelized heterogeneous permeability distributions permeability under waterflooding are used for showcasing the prediction and optimization performances of the deep-learning-based framework. The results from this framework are directly and quantitatively compared with the ones simulated using a commercial high-fidelity reservoir simulator.
Unconventional Field Testing with Clamp-on Multiphase Flowmeter
Authors: Black Michael , Heinks Carsten , Cramer Ron 
Published: September 2024

Abstract
Following on from a preliminary set of field testing performed at a selection of USA based unconventional wells longer term measurements with a clamp-on Electromagnetic Acoustic Transducer (EMAT) multiphase metering system shall be presented. Longer term trends and correlations between the well behavior and the meter data shall be discussed including opportunities for asset optimization. The clamp-on meter can be rapidly installed on the production flow lines of new wells or retrofitted to existing wells with zero intervention. The EMAT measurement approach is clamp-on and non-radioactive.
The workflow used to configure the meter prior to installation to the line shall be described. This includes empirical and physics-based flow analysis of reported well conditions based on pipe geometry, production data and PVT information. Following this, EMAT flowmeters were brought to multiple well sites and installed on producing flowlines downstream of the well heads where measurements were taken. Results obtained were compared against expected flow conditions over longer time periods than previously presented.
EMAT flowmeters were installed on several unconventional wells with an installation time less than one hour and with zero intervention. Multiphase flow measurement results from these unconventional wells shall be presented and compared expected well behaviors. The flexibility of the clamp-on deployment allows measurement across multiple wells within a field or reservoir. How this can be applied to real time production optimization and surveillance shall also be explained. In preparation for each well, simulations were performed using the Taitel and Dukler model to predict the flow regime. More detailed physics-based simulations were performed to understand the flow behavior of the individual phases. It is possible with the clamp-on meter to monitor the flow regime and quickly detect flow regime changes within the pipe through monitoring of diagnostic parameters: these shall be compared against the predicted behavior of each of the unconventional wells. Furthermore, the longer-term nature of the measurement allows insight into longer time constant behavior changes of wells and fields leading to further insight into optimization for production and assets.
The flexible deployment of the EMAT multiphase flow measurement device presents potential for significant well production and HS&E improvements in oilfield surveillance and production optimization. The longer term assessment of the wells compared to previous measurements with this technology shall allow further insight into longer time constant well- and field-related phenomena which must be understood to effectively optimize production of the well.