bluuebunny/crossref_metadata_2025_split huggingface dataset:
Active Magnetic Ranging Solution While Drilling for Geothermal Loop: Test in Low-Conductive Formation
Authors: Husby K. , Hjelstuen M. , Eriksen T. J. , Saasen A. , Ytrehus J. D. , Liberale A. , Koraei M. 
Published: March 2024

Abstract
In the enterprise of creating a geothermal loop deep in a formation it may be necessary to drill an intercept with another well. This intercept has some similarities with the well intercept needed when drilling a relief well in cases of blow out of gas or oil wells. At the same time, it is necessary to avoid colliding with the other well prior to reaching the intercept point. A constant distance may be required to the other well at some well depths. A prototype of a tool for Active Magnetic Ranging While Drilling (AMR) without the use of a wireline operation has been developed to navigate for distance control or intercept. The main scope of the current article is to present the results of a prototype test of this new tool in a test well suitable to simulate geothermal well.
The ranging tool emits a low frequency alternating current into the formation to reach the target well. The current then run down the target well’s casing and back to the well being drilled. This electric current set up a variable magnetic field that is measured by the AMR tool determining the distance to and the direction towards the target well. Hence, the tool facilitates drilling radiator shape multi-lateral well paths. If drilling an intercept at the end of the radiator sections, 10 - 25 wireline runs are needed in each lateral to the target well is intercepted. The present AMR tool is fully integrated in the drill pipe and, thus, all the tripping operations are avoided.
A prototype of an active magnetic ranging tool on the drill pipe has been developed. This tool design is outlined in the paper. The focus is given to performance tests conducted in research wells in Norway. A drill pipe is placed in a vertical well drilled in a gneiss formation. This well simulates a target well. The AMR tool was run in a parallel well and the direction and distance to the nearby target well was measured. The gneiss formation would be a tough formation to drill. However, this formation type is suitable for testing drilling in geothermal well formations. The set-up and the results of this logging operation conducted on a drill pipe is described in detail. It is shown how the direction and distance between the two wells are measured using the tool.
An In-Depth Evaluation of Toe-To-Heel Air Injection Application in a Heavy Oil Reservoir Underlain by Bottom Water. Kerrobert Case
Authors: Turta Alex , Singhal Ashok K. , Islam Mohammad N. , Greaves Malcolm 
Published: March 2024

Toe-To-Heel Air Injection (THAI) is an efficient in-situ combustion (ISC) process in which a horizontal producer is located close to the bottom of an oil layer with its toe close to a vertical air injection well. Once initiated, the ISC front propagates from the toe to the heel region of the horizontal section of the producer. THAI provides more control over the direction of the ISC front propagation (guided by the horizontal section of the producer), and it preserves the in-situ upgrading of the oil due to its short-distance oil displacement feature. Because of its controlled gas-liquid segregation, THAI is designed to mitigate the severe override experienced in conventional ISC processes.
The Kerrobert THAI Project represented the second testing of THAI in the field, and it was designed and implemented by Petrobank Energy and Resources ("Petrobank"), based on the THAI patent (US Patent No. 5626191, 1997, Canada Patent No. 2176639, 2000). The patents basically describe two separate well applications: a direct line drive (DLD) configuration and a staggered line drive (SLD) configuration, for which birds-eye views are provided in Figure 1 and Figure 2, respectively. An illustrative cross-section of the process is shown in Figure 3; the start-up region is shown in all these pictures.
The patents indicate that the vertical injector should be perforated high in the oil formation in both cases. The Kerrobert Project was designed to use DLD well configuration, but during the operation, SLD configuration was also tested in a few cases.
Phase Behavior Analysis of Multicomponent Diluent and Bitumen Mixtures: Applications in In-Situ Extraction and Pipeline Transportation
Authors: Khan Mohammad Shah Faisal , Hassanzadeh Hassan 
Published: March 2024

Abstract
This research investigates the phase behavior of multicomponent diluent and bitumen mixtures, providing experimental data essential for bitumen recovery and pipeline transportation. Using a custom-made experimental setup, including a high-pressure and high-temperature rocking PVT cell, densitometer, viscometer, and data acquisition system, the study analyzes homogenous mixtures and liquid-liquid equilibrium (LLE). Thermophysical properties, such as viscosity and density, are measured. The results reveal that a critical volume fraction of 0.7 of the multicomponent solvent studied leads to a phase transition from homogeneity (single-phase liquid) to two distinct liquid phases. Liquid-liquid equilibrium analysis at different solvent concentrations and temperatures demonstrates the significant impact of diluent on bitumen properties. This study addresses a gap in existing experimental data, offering crucial insights into solvent-aided bitumen recovery and pipeline transportation. It contributes to fostering more efficient and sustainable practices in the oil and bitumen industries.
Case Study of Autonomous Inflow Control Devices (AICD) to Control Water Production in Fractured Carbonate Reservoir in Eastern Montana
Authors: McConnell Matt , Jasser Rami , Tappan Jake , Fipke Steven 
Published: March 2024

Abstract
Autonomous Inflow Control Devices (AICD) have been commercially available since 2010 and there are many published case studies of wells in sandstone reservoirs, encompassing a wide variety of water and gas control scenarios. There are, however, relatively few documented studies of AICD for water control in fractured carbonate reservoirs. This paper will discuss and compare two trial wells that were completed with AICD in Eastern Montana in late 2022.
Mission Canyon is a limestone formation of the Mississippian age and is associated with the Williston Basin of the North Central United States. It is composed primarily of oolitic calcarenite consisting of sedimentary carbonate grains. Depending on the grain size, matrix permeability can be relatively low, and a complex network of natural fractures is what dominates fluid flow. These fractures can be good or bad; they can deliver a good oil rate, or they can be direct conduit to the reservoir’s natural bottom-water drive. Consequently, most wells start with 50% water cut and reach 90%+ within a few months. Water volumes can be as high a 3000 barrels of water per day, per well, requiring large-volume pumps and significant water handling & disposal infrastructure. This field trial of AICD technology was designed to reduce water production rates, and then evaluate the impact on additional oil recovery.
The technology tested was an AICV (autonomous inflow control valve) that utilizes the fluid viscosity and density to autonomously restrict water. The AICV devices were installed in two wells; the first was an existing horizontal producer (retrofit installation) and the second was a newly drilled horizontal well. AICV were installed on 3-1/2" liners in the open-hole horizontal section, compartmentalized into stages by swellable packers. Each compartment of the well was approximately 150 ft long and was controlled by a dual AICV flow screen. Nodal analysis predicted a 50% reduction in produced water, with an increase of 20-30% oil rate versus the standard open-hole laterals.
This field study documents the world’s first application of the AICV technology for light oil, water control in a fractured carbonate reservoir. Many valuable lessons were learned on how to best complete and operate the wells. Preliminary results show effective water control, regardless of pressure drawdown. The paper will share production results for each well and attempt to estimate the net effect on oil production and well economics.
Integrating Fracture and Reservoir Modelling to Evaluate Fracture Geometry and Reservoir Performance – A Duvernay Case Study
Authors: Taylor Robert Stewart , Joslin Kevin , Alexander Neal , Jackson Jared 
Published: March 2024

Abstract
Hydraulic fracture modelling and reservoir simulation modelling both have many benefits for understanding the performance of induced fractures and the reservoir. However, these modelling practices are rarely used together. By integrating these workflows, it enables a more comprehensive and accurate understanding of how both primary and complex induced fractures contribute to production. In this study, a Duvernay case history was investigated where fracture and compositional reservoir simulation were both applied as one integrated workflow.
Initially the two workflows were conducted independently. The complete pumping schedule was used in a fracture simulator to predict the number, size, shape, and conductivity of the hydraulic fractures. To properly characterize reservoir flow with a gas-condensate fluid system having complex phase behaviour, a compositional reservoir simulator was required. The reservoir simulation model was history matched against field production data to determine which fractures were contributing to production. Following the history match, new fracture designs were evaluated in the fracture treatment software to better improve cluster and stimulation efficiency. These second- generation optimized fracturing treatment designs were then evaluated in the reservoir model in an iterative process to determine the impact more accurately on production rates, decline curves, and resulting cumulative production.
While fracture modelling provided detailed predictive capability for fracturing treatment design, compositional reservoir production matching contributed a new level of understanding to determine which fractures were contributing to production. The use of reservoir modelling for production matching provided a methodology of reverse engineering the most likely average values for fracture half length, number of effective fractures contributing to flow, and fracture conductivity. A new optimal fracture treatment design was developed by designing it in the fracturing software and evaluating the performance in the reservoir simulator.
This integrated workflow provides insights into limitations of the fracturing treatment design pumped, and what further predictive fracture modelling should be undertaken to further optimize future treatments. The net result provides operators with a new opportunity for increased production and profitability.
Enhancing Performance of Co2-Based CSI Process by Water Flooding and/or Nanoparticle Solution Flooding
Authors: Li Yishu , Du Zhongwei , Wang Bo , Ding Jiasheng , Zeng Fanhua 
Published: March 2024

Abstract
Foamy oil flow is a pivotal aspect of the cyclic solvent injection (CSI) process, yet the influence of water and foam stabilizers, such as nanoparticles, on its efficacy remains insufficiently elucidated. Consequently, a profound comprehension of how waterflooding and nanoparticles impact CSI performance is imperative for advancing oil recovery strategies. This study conducted a meticulously designed experimental investigation to investigate the roles of water and nanoparticles in a CO2-based CSI process. Three distinct tests were executed utilizing a cylindrical sandpack at varying injection pressures. Test 1 entailed a standard CO2-based CSI process for baseline comparison. Test 2 involved a waterflooding process with an injection volume of 1.5 PV, succeeded by a CO2-based CSI process. Test 3 featured a hybrid process comprising the sequence: CSI-waterflooding-CSI-Nanoparticle solution flooding-CSI. Key parameters including injection rate, injection volume, sandpack pressure, production rate, cumulative production, and water cut were meticulously monitored and recorded. Thorough data analytics were then employed to scrutinize the impact of water and nanoparticles, identifying mechanisms for enhancing the CSI process. Laboratory results revealed that the total oil recovery in Test 2 CSI process exceeded that of Test 1 by 7.9%, underscoring the increased efficiency of Test 2. This efficiency was attributed to a 33.2% lower oil saturation after the waterflooding process in Test 2 compared to Test 1. The positive impact of waterflooding on CO2-based CSI processes extended to Test 3, where the oil recovery factor of the CSI phase following waterflooding increased by 7.1% compared to the pre-waterflooding CSI phase. After nanoparticle solution flooding, the subsequent CSI phase yielded an additional 5.9% original oil in place (OOIP), demonstrating the nanoparticles' capacity to enhance foam stability even after multiple second oil recovery (SOR)/ enhanced oil recovery (EOR) processes. As the combined process progressed, the instantaneous gas-oil-ratio increased, facilitated by expanded space for CO2 injection amid heavy oil production. The amalgamated process achieved an impressive total oil recovery factor of 69.5%, more than doubling that of the CSI process in isolation.
Optimizing Completion Design for Delaware Basin Wells with Real-Time Performance Monitoring
Authors: Assem A. I. , Ibrahim A. F. , Sinkey M. , Johnston T. , Marouf S. 
Published: March 2024

Abstract
The performance of shale wells hinges significantly on the Stimulated Reservoir Volume (SRV) generated through hydraulic fracture operations, where the ratio of fluid to proppant per foot is critical for enhancing recovery. Shale well production rates are intricately tied to the Stimulated Rock Volume achieved during fracture treatments, with completion design also playing a pivotal role in optimizing SRV for individual wells.
This paper concentrates on refining completion design for Delaware Basin wells by leveraging real-time performance monitoring. The objective is to enhance overall completion design effectiveness by dynamically adjusting injected fluid volumes based on continuous monitoring of well performance. In the initial completion design for the first well on the pad, a fluid volume of 50 bbl/ft was employed. However, despite monitoring the stimulated fracture surface area, no observed fracture hits during injection and post-stage fall-off analysis suggested the potential for improvement. This led to the hypothesis that increasing the injected fluid volume to 60 bbl/ft could be beneficial.
The adjusted completion design, featuring an increased fluid volume, was implemented in the subsequent well. The stimulated surface area in these wells exceeded the initially calculated surface area in the first well, supporting the hypothesis that the increased fluid volume enhances fracture stimulation. Three months into production, the performance of the second well validated the optimized completion design. This well demonstrated higher production compared to the first, with an increase from 50 bbl/ft to 60 bbl/ft in injected fluid volume. This aligns with surface area estimations, providing tangible evidence of the advantages derived from optimizing completion design through real-time monitoring.
These findings emphasize the significance of considering stimulated surface area in the design process and underscores the crucial role of real-time well performance prediction and the adaptive adjustment of completion design parameters in maximizing production efficiency in the Delaware Basin.
Utilizing CO2 for Energy Extraction from Geothermal Reservoirs in the Baram Basin, Sarawak Using Numerical Reservoir Simulation Based on Analogue Data
Authors: Bataee M. , Rajandran K. V. , Soh M. , Ruvalcaba J. B. , Hamdi Z. , Carter R. 
Published: March 2024

Abstract
This study investigates the sustainable utilization of CO2 for energy extraction from geothermal reservoirs. Geothermal energy is a reliable and renewable source, but its efficiency can be enhanced through innovative approaches. The concept of utilizing CO2 as a working fluid in geothermal systems holds promise due to its favorable thermodynamic properties and potential for CO2 capture and storage. This research aims to explore the feasibility and benefits of using CO2 for energy extraction from geothermal reservoirs. The study combines theoretical modeling and numerical simulations to assess the performance of CO2-based geothermal systems. A conceptual framework is developed, considering the thermodynamic behavior of CO2 and its interactions with the subsurface reservoir. The simulations involve reservoir characterization, fluid flow analysis, and heat transfer calculations, taking into account various operational parameters and system configurations. The results demonstrate the potential of utilizing CO2 for energy extraction from geothermal reservoirs. The simulations reveal enhanced heat transfer efficiency and increased power generation when compared to traditional geothermal systems. The utilization of CO2 as a working fluid facilitates higher thermal efficiencies, lower greenhouse gas emissions, and improved overall system performance. The results also highlight the importance of proper reservoir characterization and operational optimization for maximizing energy extraction potential. The findings of this study emphasize the sustainable and efficient utilization of CO2 for energy extraction in geothermal systems. By employing CO2 as a working fluid, geothermal power generation can be significantly enhanced, contributing to a more sustainable and carbon-neutral energy sector. The outcomes of this research provide insights into the technical feasibility and environmental advantages of CO2-based geothermal systems, serving as a basis for further development and implementation of this innovative approach. The study contributes to the ongoing efforts in harnessing renewable energy sources and reducing carbon emissions, advancing the field of geothermal energy and promoting a sustainable energy transition.
Analysis of Energy-Economic Dynamics in Canadian Provinces: Insights from Data Analytics
Authors: Gbakon K. , Ajienka J. A. , Iledare O. , Gogo J. 
Published: March 2024

Abstract
This paper conducts study of the relationship between end-use energy demand and economic growth as indicated by the Gross Domestic Product (GDP) on a province-level within Canada. There is abundant literature investigating the direction of causality between energy use and economic growth. However, these studies tend to focus on country units or groupings of countries. Additionally, the direction of causality between energy and economic growth has yielded mixed results, which depend on the econometric methods used, the time period, and the inclusion of other economic growth/productivity factors, such as labour and capital. The contribution which this paper seeks to make is to determine the relationship between GDP and energy use within the sub-national units of Canada. This is especially important to help shape the broader conversation about the potential distributional impact of climate change policies on the economic well-being of the sub-nationals of Canada. This paper utilizes the well-known Cobb – Douglas functional form to examine the relationship between inputs, end-use energy demand, and output, defined as GDP for the provinces. Population, End Use Energy Demand and provincial level GDP time series data from 1995 to 2021 are obtained from StatsCan. Our descriptive data analysis revealed that the highly ranked provinces by growth rate of energy use per capita were not necessarily top ranking by GDP per capita growth rate. Taken together, since year 2000, 10 of out of Canada’s 13 provinces and territories have achieved growth in GDP per capita while reducing their Energy use per capita. Additionally, Granger causality showed broadly that the sub-nationals of Canada can be classed majorly as either having neutral relationship between energy and economic growth (46%) or are in the category where economic growth drives the energy use per capita (39%).
Prediction of Mineralogical Composition in Heterogeneous Unconventional Reservoirs: Comparisons Between Data-Driven and Chemistry-Based Models
Authors: Yang Zhengru , Ghanizadeh Amin , Younis Adnan , Clarkson Christopher R. 
Published: March 2024

Abstract
Prediction of mineralogical compositions along multi-fractured horizontal wells (MFHWs) using indirect methods, for the purpose of characterizing lithological and rock brittleness heterogeneity, is appealing due to the challenges associated with direct mineralogical evaluation. This study aims to 1) develop predictive machine learning models for indirect estimation of mineralogical compositions from elemental compositions, 2) compare mineralogical compositions obtained from data-driven and chemistry-based approaches, and 3) provide practical recommendations for fine-tuning and training of data-driven models.
Leveraging recent advances in deep learning, an attention-based gated recurrent unit (AttnGRU) with a "feature extractor-post processor" architecture was developed for predicting compositions of ten primary minerals based on elemental data. For comparison, classic regression-based and ensemble learning models including support vector regression (SVR), random forest (RF), and a feedforward neuron network (FFNN) were utilized. Data-driven models were trained and tested using XRD data measured on 217 samples from the Montney Formation, and the outcomes were compared to those derived from stoichiometric material balance equations (a previously-developed chemistry-based model) to evaluate the effectiveness and capabilities of different predictive approaches.
The data-driven models consistently outperformed the chemistry-based method with significantly lower mean absolute error (MAE) and higher R2. The predictive performance order was FFNN ≥ AttnGRU &gt; RF &gt; SVR &gt;&gt; chemistry-based model, with MAE = 1.05, 1.09, 1.24, 1.35, and 2.46 wt.%, respectively. Importantly, FFNN, AttnGRU and RF offered more accurate predictions of chlorite and illite, which are known to negatively affect reservoir quality. This indicates the superior performance of the three models for reservoir characterization applications. Furthermore, AttnGRU exhibited greater robustness than the other two models, with less sensitivity to overfitting issues. Data-driven models displayed different levels of performance when decreasing training dataset size. It is recommended that, in order to achieve reasonable predictions for the studied reservoir with data-driven approaches, more than 50 training samples be used. It is further observed that data-driven models exhibited limited predictive capability (MAEs ranging from 3.02-3.45 wt.%) when applied to a synthetic "global dataset" comprised of samples from various formations.
Through the comparison of multiple independent datasets (XRF-derived chemistry-based, XRF-derived data-driven, XRD) collected on identical samples, this work highlights the strengths, limitations, and capabilities of different machine learning techniques for along-well estimation of mineralogical composition to assist with reservoir characterization.