bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decarbonizing the Residential Sector with Geothermal District Heating Networks: The Contribution of Hydrocarbon Wells
Authors: Alimonti C. , Scrocca D. 
Published: June 2024

Abstract
Climate change and the energy crises are forcing industrialized countries to contain CO2 emissions and use indigenous renewable energy sources. Geothermal energy has great potential, especially thermal energy, given that 48% of the final energy consumption in the EU20 countries in 2021 concerns heating and cooling systems. The studies on the potential development of district heating (DH) in Italy mainly consider using renewable sources and waste heat from industrial plants. This study highlights how the recovery and reuse of existing hydrocarbon infrastructure can contribute to the diffusion of DH projects that implement the principles of the circular economy.
The Italian geothermal potential in terms of repurposing oil wells is essential. In the 111 onshore hydrocarbon production concessions, 847 active wells have different operational statuses, including 436 productive wells and 399 potentially productive wells.
This study presents two examples of reusing hydrocarbon wells to satisfy the energy demand for heating residential buildings through locally available geothermal renewable energy sources. This study has verified the feasibility, pros, and cons of developing geothermal DH. The context is related to depleted hydrocarbon fields, where oil wells can be converted into geothermal wells.
Regarding the economic sustainability of repurposing projects, the results of this study highlight the viability and profitability of DH development. The analysis based on evaluating the payback time (PBT), the project net present value (NPV), and the internal rate of return (IRR) highlights the dependency on the heat tariff. The evaluated levelized cost of heat (LCOH) has been assumed as a key performance index. It should also be noted that LCOH for the two case studies is less than the reference tariff of 100.5 €/kWh. This result is obtained mainly from the reduced capital expenditure for drilling. However, our results show how reusing hydrocarbon wells in geothermal projects constitutes an excellent opportunity to access geothermal resources, significantly reducing the necessary investment and the mining risk and strongly improving the economics of the projects.
The solutions adopted in this study can also be optimized by considering the application of a binary system to produce electricity. District heating systems using binary plants achieve greater global energy efficiency. It is necessary to revolutionize the residential heating system through solutions such as the one described in this study, creating DH systems that use a zero-emission energy source as the primary source, such as geothermal. The promotion and diffusion of geothermal district heating networks could significantly contribute to achieving the climate objectives set by the European Union.
Automated Rate Control to Prevent Induced Fracturing and Fracture Opening Monitored with Step Rate Tests
Authors: Ambrus A. , Mugisha J. , Shchipanov A. , Aarsnes U. J. F. , Øverland A. M. 
Published: June 2024

AbstractDesigned and occasionally induced fracturing occur during injection and production operations in petroleum, geothermal energy, and geological CO2 storage. Although the goals and perspectives vary across industries, fracture monitoring and control remains crucial. Induced fracturing enhances petroleum and geothermal energy production, whereas its prevention is a key challenge in CO2 storage. Therefore, incorporating fracture management into automated injection control would be beneficial as enabled by the recent wide deployment of permanent well surveillance technologies.This paper presents a new automated injection control approach based on on-the-fly interpretation of step-rate tests (SRT) commonly used in induced fracture monitoring. The paper explores single-phase isothermal water injection, which is relevant for describing water injection in hydrocarbon reservoirs (after forming the invaded area around the wells) and in saline aquifers. The injection control approach employs time-lapse pressure transient analysis (PTA-SRT) for SRT interpretation and constructing safe operating envelopes (SOE) representing the no-fracture response based on pressure derivative curves. When deviations from the SOE happen, the controller automatically reduces the injection rate to limit fracture growth, and continuously monitors the pressure response for the updated rate.The paper demonstrates the results of testing of the injection control approach with step-rate test data from synthetic well simulations and a real case for water injection in an oil-bearing formation on the Norwegian Continental Shelf. The simulations use a proxy model for pressure-dependent fracture permeability matched against field measurements. The testing results confirmed the effectiveness of the control approach in detecting induced fractures shortly after their onset and preventing further fracture growth by applying immediate rate adjustments. Through successive rate changes and monitoring of the pressure response, the controller can optimize the injection rate while keeping the well pressure within the safe operating envelope.The automated injection control workflow differs from existing industry practices through the integration of PTA with the safe operating envelope concept. Fracture detection with the PTA-SRT approach only requires one rate step after fracture opening, whereas standard industry workflows such as p-Q curve analysis typically requires 2-3 rate steps. In addition, the PTA-SRT approach is resistant to noise, step duration and rate stepping, making it appealing to real field SRT applications.
How Resource Classification Systems can Enable the Energy Transition
Authors: Jones A. , Ahadjie J. , Betts I. , Brinnen K. , Hartung M. , Heiberg S. , Ingvald E. 
Published: June 2024

Abstract
The first aim of this work is to show how resource classification systems, such as the Petroleum Resources Management System (PRMS), have provided essential information in the development of traditional energy resources. The challenges of the energy transition will present new demands on stakeholders and on their interactions. The second aim is to understand the new types of common information which the stakeholders will require and how resource classification can address this.
We begin by reviewing the success of systems such as PRMS and how the standardized/common information they provide has assisted in the development of resources. Next we consider the new demands which the energy transition and the Sustainable Development Goals place on stakeholders, and the interactions between them, and examine the types of common project information which will enable them to meet the challenges.
We show how established resource classification has provided key information for capital allocators to facilitate financing, for industry to manage and progress projects, and for governments to manage and improve resource development. Achieving a sustainable energy transition requires effective Public-Private Partnerships. These will be characterized by cooperation between governments, industry, and investors, with support from the public. Whilst integrated solutions are the goal, the focus should be on informed dialogue so as to make mutually beneficial decisions, and to develop the necessary, evolving capabilities. Such a dialogue requires common information. Classification systems can provide this through the expansion of common metrics. Common project metrics are included in the United Nations Framework Classification for Resources (UNFC), which is bridged to other classification systems, including PRMS. These include not only physical quantities of production but also measures of economic, environmental and social costs and benefits, and require consideration of variations with time and cumulative quantities.
The work shows that additional common information can be incorporated into UNFC, so it is an information framework for decisions. This will support dialogue between stakeholders to develop effective Public-Private Partnerships and meet the demands of the energy transition. It shows the potential for professional bodies (such as SPE), academia and industry to contribute to the operationalization of the expanded metrics and to merge the results between classification systems, including with PRMS. In this way, stakeholders and the UN have an opportunity to work together to help facilitate the next stage of energy provision for sustainable development.
Exploring North Sea Fractured Sandstone Properties: Artificial Intelligence, Multiscale Imaging, Pore-Fracture Network Analysis and Experimental Results
Authors: Panaitescu C. T. , Wu K. , Kartal M. E. , Tanino Y. , Starkey A. , Qin G. , Zhao L. , Cao Z. , Wu S. 
Published: June 2024

Abstract
This paper presents a novel integrated workflow that enhances the understanding of matrix pore-fracture flow in lower-margin reservoir engineering. The workflow, which is applied to a North Sea reservoir core sample, combines experiments, imaging, deep-learning segmentation, and pore-scale simulation techniques. Advanced Artificial Intelligence (AI) models are used to analyse images from fractured and unfractured micro-computed tomography (micro-CT) scans. This enables a comprehensive multi-scale analysis crucial for optimising production in challenging reservoirs.
The study commences with an examination of a relatively clean sandstone sample from a depleted North Sea sandstone hydrocarbon reservoir. A specially developed geomechanical-flow experimental cell induces and monitors fractures, offering critical insights. Post-fracture, in-situ imaging accurately captures fracture geometry. The analysis is further enhanced by AI-powered segmentation of image pairs, followed by a multiscale pore-network analysis, which experimentally validates the fracturing-flow processes.
This study's findings have significant implications for reservoir development. By demonstrating how multi-scale, image-derived data can enhance understanding of porous features, the study provides a valuable tool for more efficient resource extraction in marginal fields. The workflow, which includes two-dimensional (2D) and three-dimensional (3D) deep convolutional neural networks (CNNs) with tailored objective functions and a novel algorithm for large-scale domain decomposition and pore network extraction, improves core-scale fracture-pore network modelling (fracture-PNM). The fluid simulation reveals intricate flow behaviours in matrix, fracture, and combined systems, offering crucial insights for advancing subsurface geo-energy processes like hydraulic fracturing, carbon and hydrogen storage, and deep geothermal energy systems.
We introduce cutting-edge segmentation models using 2D and 3D CNNs tailored for multi-scale analysis of fractured systems. A novel 3D large-image PNM extraction and domain decomposition algorithm is proposed, enhancing the fidelity of core-scale PNM modelling. The study offers new perspectives on matrix-fracture flow mechanisms through experimentally validated modelling, enriching the current understanding of fluid dynamics in complex subsurface environments.
Analysis of the Performance of Horizontal Wells in the Long-Term CO2 Sequestration in Saline Aquifers
Authors: Moridis G. J. , Reagan M. T. , Huang T. , Blasingame T. A. 
Published: June 2024

Abstract
The objectives of this study are (a) to analyze the CO2 sequestration performance of horizontal wells in saline aquifers during both the injection period and the subsequent long ‘rest’ period of inactivity, (b) to investigate the potential impact of salinity on the well injectivity, (c) to evaluate the effects of key well and aquifer properties, system conditions, and injection practices, and (d) compare the sequestration performance of horizontal wells to that of vertical wells. The study accounts for all known non-isothermal multiphase flow and transport processes associated with CO2 sequestration, including the potential salt (halite) precipitation, and uses a high-resolution grid to accurately capture the possible emergence and effects of halite near the well. The study models continuous CO2 injection at realistic rates into varying-salinity aquifers until a geomechanically safe maximum pressure is reached, followed by 100 years of shut-in. The results show that (a) large-scale CO2 storage in saline aquifers using horizontal wells is technically feasible, (b) the storage potential of CO2 dissolved in the aqueous phase is substantial (both in terms of the magnitude of the dissolved concentration and its spatial extent) and increases over time during the rest period, but its benefit decreases rapidly with an increasing salinity, (c) unlike the case of vertical wells, salinity appears to have practically no effect on the well injectivity as halite precipitation is negligible even at high CO2 injection rates, requiring no mitigation measures, and (d) horizontal wells appear significantly (if not overwhelmingly) superior to vertical wells for CO2 sequestration in saline aquifers. The results of the study indicate that the overall CO2 sequestration performance of horizontal wells (a) is significantly greater for deeper saline aquifers and, surprisingly, for more permeable upper boundaries, and (b) improves modestly with a decreasing CO2 injection rate, an increasing aquifer permeability, an increasing aquifer salinity, and by positioning the horizontal well closer to the base of the aquifer, but (c) appears practically insensitive to the temperature of the injected CO2. With the exception of the cases of very high-salinity and of a more permeable upper boundary, the long-term amount of CO2 dissolved in the aqueous phase routinely exceeds 45-50% of the total CO2 amount stored in the aquifer. Additionally, with the exception of the cases of more permeable upper boundaries, the long-term losses into the overburden never exceed 1% of the injected CO2. More permeable overburdens may register losses as high as 5%, but these can be acceptable because they occur mostly in the aqueous phase, stagnate very rapidly as they ascend in the subsurface profile—where they pose a limited risk for CO2 exsolution because of lower temperatures—and are associated with much larger amounts of sequestered CO2.
Enhancing Drilling Efficiency for Reduced CO2 Emissions Through Optimization of Mud Motor Power Section
Authors: Belov D. , Du W. , Cantarelli E. , Le K. 
Published: June 2024

Abstract
An approach that combines data-driven analysis and modeling was used to select the optimal mud motor power section tailored to specific drilling requirements. Additionally, a novel methodology was developed for estimating the CO2 footprint of drilling operations based on operational data. Practical demonstrations verified that the strategic utilization of the optimal power section enhances drilling efficiency and mitigates the CO2 impact associated with drilling processes.
Our focus revolves around choosing the optimal power section for the drilling, considering both drilling conditions and limits. Additionally, we estimatethe CO2 footprint for the entire drilling process after the job to underscore the positive impact of selecting the right power section. To achieve these goals, we integrate an advanced physical model of the power section with the capabilities of machine learning and data science. This power section model empowers us to predict its performance and durability in advance, facilitating an optimal choice based on expected drilling conditions. The established workflow for CO2 footprint estimation utilizes surface drilling data, ensuring precise results.
To optimize the power section for drilling, we employ a modeling method coupled with a machine-learning approach. This aids in selecting a suitable power section type and determining drilling parameters based on specific requirements and equipment specifications, contributing to heightened drilling efficiency.
Leveraging digital capabilities enables strategic implementation to minimize greenhouse gas emissions. Enhancing drilling efficiency via optimal mud motor power section selection improves rate of penetration, reduces nonproductive time, and substantially cuts field failures. This cumulatively shortens total drilling time, leading to both direct and indirect CO2 emissions reductions. To gauge the carbon footprint during drilling operations, we construct a physical model. This model predicts rig power consumption by considering surface drilling parameters (as pressure, flow rate, etc.). It estimates power consumption by components like mud pumps, top drive, and draw works, using system efficiency data. Additionally, a model encompassing generator sets and diesel combustion is employed to estimate CO2 emissions.
Application of the proposed methodology to a real field example demonstrated the impact of enhanced drilling performance with optimal power sections versus conventional ones, along with effective greenhouse gas emissions reduction. The entirety of the results and conclusions highlights the substantial value of digital technologies and a smart approach in selecting drilling equipment for the energy transition.
Investigating Rock Material Impact on Low Salinity Water Flooding: Experimental Study with Chalk Samples of a Danish North Sea Oil Reservoir
Authors: Mokhtari R. , Afrough A. , Talaei A. , Feilberg K. L. 
Published: June 2024

Abstract
The low salinity water-flooding technique, an enhanced oil recovery (EOR) method, alters the initial crude oil/brine/rock (COBR) equilibrium, enhancing oil mobilization and potentially reducing CO2 footprint of oil production. Uncertainties arise with natural crude oils and reactive rock surfaces, such as chalk. A study on low salinity waterflooding in Danish Chalk reservoirs yielded results differing from existing literature. To further investigate these results, core flooding experiments were conducted using diverse chalk samples, including outcrop material, dry-stored reservoir core plugs, and preserved reservoir core samples. It's crucial to highlight that existing literature data predominantly relies on outcrop sample experiments, potentially limiting their relevance to reservoir conditions. Computed tomography (CT) images were captured before the experiments to assess rock integrity. Nuclear Magnetic Resonance (NMR) measurements post-experiments gauged the average wettability of the core plug. Contact angle measurements assessed wettability alteration, and ion chromatography analyzed the effluent brine samples. Coreflooding outcomes revealed that preserved core samples initially exhibited an oil-wet state, reflective of reservoir conditions, but underwent changes post-waterflooding and cleaning. This renders direct comparisons challenging, making these cores non-reusable. Conversely, dry-stored reservoir core plugs maintained consistent wetting states across multiple experiments, ensuring reusability and facilitating repeatable, comparable results. However, outcrop samples yielded fundamentally dissimilar results from reservoir samples, proving inadequate analogs. Contact angle measurements highlighted significant differences in wetting conditions and wettability alteration between outcrop and reservoir samples. This discrepancy explains why the negative impact of sulfate ions was evident only in reservoir samples. Ion chromatography results revealed calcite dissolution and concurrent magnesium precipitation during low salinity water injection. While extensively demonstrated in sandstones and some carbonates, the application of low salinity water flooding faces significant challenges in carbonate reservoirs, especially chalk formations. Moreover, the current reported findings regarding chalk samples rely on experiments conducted using outcrop samples. Consequently, there is a lack of experimental data on real reservoir material in existing literature. The conflicting outcomes compared to the literature underscore the significance of conducting experiments on actual reservoir material.
Enhancing Short-Term Production Forecast in Oil Fields: Integrating Data-Driven and Model-Based Approaches to Reduce Uncertainty
Authors: Gonçalves M. M. , Werneck R. , Castro M. , Amaral M. , Mendes P. Ribeiro , Filho L. A. Lusquino , Esmin A. , Moura R. , Morais E. , Linares O. C. , Lustosa A. , Salavati S. , Schiozer D. J. , Ferreira A. Mello , Rocha A. , Davolio A. 
Published: June 2024

Abstract
Reservoir simulation models are usually applied to optimize oil field production across its life cycle but face challenges in short-term forecasting. Data-driven techniques (DD) show promise for short-term predictions but lack reliability over extended periods. This study introduces a Hybrid Methodology (HyM) combining the optimal features of model-based (MB) with DD approaches to select the best simulation models to make short-term decisions, effectively reducing uncertainty in short-term production forecasts for a real field. Using DD techniques, such as Transformers, we forecast 150 days of cumulative water, oil, and gas production for each well of a real field based only on historical production. We also run production forecasts using 200 history-matched simulation models for this field. In our HyM methodology, we propose to select the best simulation models according to their fit to the DD forecasts, assuming the DD forecasts to be more representative of the short-term behavior of the real field. We compare our HyM to a conventional MB model selection based on history-matching quality, referred to as MB. The proposed approach was applied to a heavy oil field from the Campos basin (Brazil), a deep-water turbiditic reservoir. We separated five months of real history data as the ground truth to rank the selected models through the forecast error and rank similarity. With this evaluation, we measured how accurate the forecast of the chosen simulation models was and the efficacy of each approach (HyM and MB) in ranking the simulation models. Our results showed that the subset of models selected using HyM successfully outperformed those chosen by the MB approach since they are better fitted for short-term forecasts for oil, water, and gas rates. The proposed HyM method yields a 75% error reduction, considering the rank similarity, with respect to the MB approach. Consequently, we conclude that better results may be achieved by using a DD forecast as a reference to reduce the uncertainty of oil, water, and gas rates (HyM approach) instead of using the actual history (MB approach). The new methodology showed promising results by selecting models with attenuated errors for oil/water rates during the history-forecast transition, complementing the data assimilation procedure. This study presents an innovative methodology merging the strengths of machine learning (ML) and simulation models to enhance the reliability of short-term production forecasts. The proposed approach is versatile, not tied to any specific ML algorithm, and effectively minimizes uncertainties, particularly in complex fields with numerous wells.