bluuebunny/crossref_metadata_2025 huggingface dataset:
A legacy of solidaric critique and hope for a better world: A meditation on the scholarly contribution of Ronald Sultana
Authors: Hooley Tristram , Thomsen Rie , Debono Manwel
Published: April 2024

In this article, we reflect on the legacy of Ronald Sultana who died on Friday 24th November 2023. We argue that he was a major figure in the development of career guidance theory and practice of the late 20th and early 21st century. The article begins by reflecting on and summarising his contribution to the fields of education and career guidance. While we focus on his contribution in the field of career guidance, we also acknowledge that this work sits in a wider body of work in which Sultana’s sociological imagination addressed the operation of the education system around the Mediterranean, across Europe and in the Global South. We explore Sultana’s contribution to theory, policy and practice. We argue that in relation to theory he situated careers in context, carefully traced the range of political roles that career guidance could play and argued for career guidance as a force for social justice. In relation to policy, we highlight his role in international reviews and his critical commentary on this kind of policy borrowing and lending. In relation to practice, we focus on Sultana’s development of resources and study programmes promoting lifelong guidance, and the leveraging of networking to develop a united community of professionals. Finally, we consider the challenges that Ronald left for us and discuss what those who seek to build on his legacy could do.
The Chicago School of Sociology and the origins of career studies
Authors: McCash Phil
Published: April 2024

This article troubles the origin myth of career development centred on the vocational guidance movement. It draws attention to the early theorisation of career undertaken mainly, although not exclusively, within the pioneering Chicago School of Sociology in the early and middle part of the twentieth century. It is argued that this interactionist tradition within sociology gives the field of career development a coherence and integrity that might otherwise be missed. In addition, it is suggested that its underpinning philosophy is particularly suited to embracing theoretical innovation from other disciplines. The formal study of career starts in sociology, perhaps, but it continues to expand as a transdisciplinary field of enquiry.
Career development: A geographical perspective
Authors: Alexander Rosie
Published: April 2024

Career theory has typically focused on the ways that careers develop over time rather than thinking about the role of place. Drawing from insights from the geographical literature, this paper argues for a much greater attention to the spatial dimension of career development, understanding that place is a fundamental dimension of human existence, and that our careers are enacted in dynamic relation to the places we live in and move through. Further consideration of spatial dynamics, especially in relation to inequalities, should not therefore be considered a peripheral endeavor, but central to elaborating our theoretical understandings of career development.
Developing interdisciplinary habits of hand, heart and mind for career development practitioners
Authors: Frigerio Gill
Published: April 2024

Within an edition of this journal that is looking at the contribution of differing disciplines to the study and practice career development, this article explores the notion of career as explicitly interdisciplinary, and examines different understandings and implications of that.  From here, it uses Schulman’s ideas of surface, deep and implicit signature pedagogies to consider the implications of our interdisciplinarity for the ‘signature pedagogies’ involved in the training of career development practitioners. It ends with scoping out some priorities for further consideration and arguing for NICEC’s role in articulating the habits of hand, heart and mind involved.
The effect of design-based STEM learning on students' scientific creativity in solar energy topic
Authors: Lidya Nala , Habibah Hulwah , Suryadi Ahmad
Published: April 2024

This study aims to investigate the effect of design-based STEM learning on students' scientific creativity in the context of solar energy. The study involved 64 tenth-grade students from a public Madrasah Aliyah in Jakarta, who were split into two groups: an experimental group that used design-based STEM learning and a control group that used conventional learning. Both groups were given a scientific creativity posttest in the form of seven open-ended questions. The study results showed that the experimental group's average scientific creativity score was higher than the control group's. Furthermore, the subdimensions analysis revealed that the experimental group's flexibility and originality significantly affected their scientific creativity. The study shows that design-based STEM learning positively impacts students' scientific creativity in the context of solar energy, although with a low category. This research can be a reference for future research to investigate the influence of design-based STEM learning on students' scientific creativity in various contexts.
Using a New Gradient-Based Ultra-Deep Resistivity Inversion Model to Map Oil-Water Contacts in the Gekko Fields
Authors: Riofrío K. , Clegg N. , Chatterjee A. , Bang A. , Fjeld P. H.
Published: April 2024

This paper evaluates a new ultra-deep azimuthal resistivity (UDAR) inversion model in the context of transition zones above fluid contacts, these are common in gradational resistivity settings and typically cause difficulty with previous inversion models. The new inversion model is applied to oil-water contact mapping from within a thin oil column, and the results are verified against conventional methods for oil-water contact determination.

A new gradient-based enhanced focus UDAR inversion model allows for model layers with continuously sloped resistivity profiles. Previously, approximation using multiple layers with discrete resistivity steps was required to model formations without sharply contrasting boundaries, as in transition zones above fluid contacts. We compare the performance of previous and new inversion models using real data from an extensively drilled sandstone reservoir with stepped or tilted gas/oil and oil/water contacts. The new UDAR inversion model is applied to map the oil-water contact across the entire structure, which is then compared with proven depths in exploration and geopilot wellbores.

The new inversion model improves UDAR imaging of the Gekko reservoir of the Alvheim area in Norway, allowing for geosteering of nine horizontal wellbores within a thin oil column to maximize oil and gas recovery. The reservoir is comprised of stacked turbiditic sandstones ranging in configuration from thick and homogenous to complexly layered sands between shale sequences. The application of an enhanced focus inversion is shown to better define both subsurface geology and oil-water contacts than the previous inversion model. This allows more confident differentiation of the oil-water contact from shale beds. The oil/water contact derived from UDAR imaging is found to closely match results from formation pressure gradient analysis and contact penetrations in nearby wellbores. Confident mapping of the oil-water contact reveals it has an uneven tilt across the structure.

This paper demonstrates how a new inversion model improves UDAR imaging in a gradational resistivity setting. It presents data that practicing engineers can use to evaluate its performance and limitations and demonstrates the capability to confidently map unevenly tilted oil-water contacts in homogenous oil-bearing reservoirs from within the transition zone.
Design Configuration for Autonomous Inflow Control Valve Technology for Newly Drilled Well in a Mature Field in Malaysia – Model vs Reality
Authors: Bandara Yasas W , Ismail Ismarullizam Mohd , Ng Natasya , Siew Caleb
Published: April 2024

Design of an ICD (Inflow Control Device) completion is initially determined based on the static and dynamic models prepared during pre-drilling stage of the well. However, actual well properties available post-drilled are quite different from the pre-drilled properties, and the completion must be optimized. The objective of this study is to address the challenges on pre- and post-completion designs which was conducted for a gas and water control application in redevelopment phase infill well in a mature field in Malaysia. All types of ICD completions are designed based on the static simulation for selected time steps of a dynamic simulation. The autonomous inflow control valve (AICV) is an advanced ICD that can also choke/close unwanted gas and water. The number of AICVs are determined based on the initial maximum oil production rate, despite permeability and gas/water saturation. Therefore, several sensitivities were conducted to determine optimum production rate with an AICV completion. The compartmentalization and distribution of AICVs mainly depended on the well constraints. Although, the required number of AICV are same for the post drill completion, packers and AICV joint placement were adjusted based on the drilling log data. Drilling logs indicated that permeabilities of the post-drilling were about 1/10 of the expected permeabilities of the model and high permeability zones were concentrated to localized zones compared to distributed high permeability in the model. Localized high permeable zones create challenging situations to place the required number of AICV joints in respective compartments due to length constraints. The completion design was re-optimized based on the new permeability profile and new constraints. Packers were placed in low permeability zones to maximize the effect of zonal isolation. Additional packers were installed to delay the gas transport to oil producing compartments in case of a gas breakthrough in neighboring compartment. The number of AICV joints utilized in the well was lower compared to the originally predicted number of joints due to shorter TD of the well. For the well, the number of AICV joints utilized was 38 out of 45 joints. The well started production with desired oil rate at 1500 BOPD with minimal gas and water production with productivity index (PI) 2x better than expected.
A New Approach to Cost Effective Multilateral Solutions for Mature Fields in the North Sea
Authors: Kelsey Matthew , Barron Angus , Dyrseth Vegard , Larsen Lars Petter , Williamson Michael , Saetre Geirmund
Published: April 2024

Multilateral well design has established itself as being a reliable and well proven means of extending well life and increasing overall reservoir recovery factor. Modern multilateral junctions constructed within the Norwegian North Sea generally tend to be technologically complex, requiring hydraulic and mechanical integrity to allow for monitoring and flow control of up to four independent branches. As the technology has matured and the requirement to reduce well construction costs becomes apparent, novel methods of multilateral junction construction are being considered. One such method, involving creation of an open hole lowside exit in the reservoir, was recognized as a low risk solution in terms of complexity and cost.

This design removes the requirement for a cased hole window exit, however the potential compaction effects common on the field necessitate some means of mechanical support at the junction location to maintain lateral flow. This led to a collaboration between the operator, multilateral technology (MLT) vendor and advanced composite tubular developer resulting in a simple reservoir deployment system. This system allowed placement of a composite tubular within the primary mainbore, thereby enabling a means of lateral flow access and depth referencing for lateral creation. This system was deployed twice within the field with no non-productive time (NPT) associated with the operation, leading to successful drilling and completion of two dual lateral wells.

This paper discusses design considerations around the novel ML construction method including system requirements, configurations and a review of the operations performed.
Comprehensive Investigation of Near-Wellbore Damage in Produced Water Reinjection: Bridging Microfluidic Insights and Core Flooding Test Analyses
Authors: Saikia Tinku , Kurbasov Maksim , Øye Gisle , Feilberg Karen Louise
Published: April 2024

The disposal of produced water poses a significant challenge for the oil and gas industry due to its voluminous nature and the presence of diverse contaminants, rendering treatment and disposal processes costly and intricate. Adhering to stringent environmental regulations, reinjection of produced water into reservoirs emerges as an optimal solution, not only facilitating hazardous water disposal but also aligning with pressure maintenance and oil production enhancement strategies, such as water flooding. In the Danish North Sea offshore well scenario, as delineated in the 2022 report by the Danish Energy Agency, a substantial volume of produced water is generated alongside limited oil production. The magnitude is illustrated by the production of approximately 3.7 billion m3 of oil accompanied by 30.8 billion m3 of produced water. Nevertheless, challenges persist with produced water reinjection, particularly concerning near-wellbore damage during the reinjection process. To overcome this challenge, a comprehensive understanding of formation damage mechanisms and identification of primary contributors become imperative. Conventional methods, relying on core flooding experiments involving pressure readings during injection and analytical analysis of produced water, offer insights into potential damage mechanisms. However, microfluidic experiments, offering visual analyses of damage mechanisms and pore plugging materials, provide a more nuanced perspective. This research seamlessly integrates both core flooding and microfluidic experiments utilizing Danish North Sea Field-produced water and seawater. The core flooding results unequivocally identify inorganic sediments, notably Fe3Si, as the primary cause of pore clogging. In contrast, microfluidic experiments shed light on the substantial contribution of bacterial action, specifically biofilm formation, to the intricate pore plugging mechanisms. This multi-faceted approach enhances the precision of understanding and mitigating near-wellbore damage during produced water reinjection processes.
Exploration Robot Chat: Uncovering Decades of Exploration Knowledge and Data with Conversational Large Language Models
Authors: Mosser L. , Aursand P. , Brakstad K. S. , Lehre C. , Myhre-Bakkevig J.
Published: April 2024

Hydrocarbon exploration and carbon capture and storage (CCS) evaluation are inherently multi-disciplinary tasks that require the integration of knowledge from multiple datatypes set in a historical and geological context. The diverse nature of subsurface data is often represented by a combination of direct and indirect measurements, interpretations and observations documented in multi-dimensional datasets as images, and written reports. The fidelity of these images and reports can have an enormous variety, and different qualities leading to a challenging situation where explorationists need to determine the value of a source of information while combining these sources across large spatiotemporal contexts. Modern search engines today can not only search through document text but also images. These capabilities have improved our ability to find well-known concepts based on short phrases, or keywords, combined with significant meta-data. While these types of search engines have certainly benefited practitioners, the challenge of combining information from multiple data-sources, data modalities and languages remains an open problem.

With the advent of conversational large language model (LLM) systems such as ChatGPT (Achiam et al. 2023) that provide coherent textual information and are informed by their training data, have become a reality. While ChatGPT certainly has taken many industries and their disciplines by storm, the tool is not without its shortcomings. For industry applications, in many cases the information necessary to provide answers will be highly proprietary, not shared with third parties and not part of the training data of the popular LLMs. Furthermore, due to their probabilistic nature LLMs suffer from so-called hallucinations, where the model provides a confident answer based on the user provided input but is non-factual and often non-sensical.

To answer a given user-query with factuality it is important to provide relevant information as context to the LLMs. Lewis et al. (2020) proposes combining two systems: An information retrieval system that provide relevant information to answer a given question or to solve a specific task, and a second system being an LLM that is supplemented with the retrieved information as context to answer the user's question. This pattern of so-called retrieval-augmented generation (RAG) has become highly popular in the last year due to the strong conversational capabilities of systems like ChatGPT, accessible developer APIs for interfacing with LLMs, open-source software to orchestrate RAG-systems, as well as the rapid development of open-source LLMs (Touvron et al. 2023). Moreover, since the RAG pattern does not require fine-tuning or re-training a language model, it remains one of the most accessible ways to tailor LLMs to proprietary knowledge bases.
Data-Driven Predictions of CO2 EOR Numerical Studies Using Machine Learning in an Open-Source Framework
Authors: Lliguizaca J. R. , Landa-Marbán D. , Gasda S. E. , Sandve T. H. , Alcorn Z. P.
Published: April 2024

An open-source framework is presented for the development and evaluation of machine learning (ML) assisted Data-Driven models of carbon dioxide (CO2) enhanced oil recovery (EOR) processes to predict oil production and CO2 retention. This framework generated inputs and outputs for two cases of CO2 water alternating gas (WAG) injection using Python packages and a reservoir simulator, which were subsequently used to train and test the supervised learning algorithms. The main objective was to increase the speed, robustness, and accuracy of predicting oil recovery and CO2 retention using a complete open-source approach combining Python programming, reservoir simulation, and ML techniques. This framework incorporated the reservoir model of the SPE5 benchmark study. The geometry was built using the pyopmnearwell Python package, and the simulations were run in the open-source Open Porous Media (OPM) Flow simulator. The permeability and porosity of the top layer and the gas injection rate were selected as variable input parameters to generate different settings for which the cumulative oil recovery and CO2 retention were determined using the simulator. These inputs and outputs formed the training and test dataset created for the ML model. Finally, the algorithms were optimized through hyperparameter tuning to enhance the predictive scoring metrics R-squared and Root Mean Square Error (RMSE).

Two cases were created with one injector and one producer to develop and evaluate the predictive models. Case 1 was restricted to keeping the WAG injection of 20 years with injection slugs of 3 months, and Case 2 aimed to maintain the same injected volumes for each simulation job. The reservoir simulator allowed us to produce representative results, which were then used to generate a data set for training, testing, and validating the ML algorithms. Based on the predictive scoring metrics, Gradient Boosting and Random Forest regression algorithms performed best, followed by the Decision Tree regressor. In contrast, the K-nearest neighbors regressor had a bad performance. Furthermore, two approaches for hyperparameter tuning were used to get the algorithms’ hyperparameter values, improving their RMSE or R-squared. The optimal model architecture (i.e., the best number of estimators or neighbors) was determined by the hyperparameter tuning to increase the accuracy or reduce the error of the predictions. The predictions of the data-driven models generated by DT, RF, and GBR were reliable based on the accuracy metrics of the trained and tested models, exhibiting values for R-squared above 0.93 and RMSE below 0.05. This innovative and robust approach presents a powerful tool for predicting and assessing the sensitivity of parameters of CO2 EOR projects, delivering accuracy and speed compared to existing methods.
Driving Innovation in Caesar Tonga by Managing Big Data and New Interpretations with Modern Digital Workflows, Elevating the Engineering to Enhance Asset Potential
Authors: Paclibon E. Nuñez , Glassco E. , Demichelis J. , Windham L. , Nikolaeva A. , Ogunlana D. Ajose , Reza Z. , Link E. , Shan D. , Wang Y. , Ali S. , Jageregger W.
Published: April 2024

A co-owner in the Caesar Tonga (CT) field with 13 years of production sought to replace the outdated existing reservoir simulation model with a model that could show 1) the impact of a newly installed subsea expansion, 2) future operations such as acid stimulations, and 3) the effect of offtake from a nearby field sharing the same reservoir. The model conversion objective was to optimize reservoir management strategies, enhance reservoir understanding, and streamline workflows.

A significant result of the project is the ability to compare three surface network coupling approaches from different tools. This is the first example of a single asset and single project where such a comparison has been undertaken. Future well-work activity was implemented to find the optimum balance between efficient rates and maximum recovery of this mature asset. For example, the operator's acid stimulation program/operating procedure is triggered by a minimum productivity index (PI) value and the observed individual well performance. This multiphase and crossdisciplinary approach enables assessing each workflow to identify similarities and differences in the tools and implement agile learning to each stage in the modelling.

The results of this project are implemented immediately towards the asset's long-term forecasting. The integrated platform couples the operator's subsea production system with the reservoir dynamics to optimize production efficiency. Directly integrating the subsurface with the wellbore and network through a common platform enhances communications between the co-owners. Overall, this model conversion gave us the ability to address business needs while promoting a better understanding of the asset performance.
Dynamic Reservoir Rock Typing for Supercritical CO2-Brine System in Sandstone
Authors: Akbar M. N. A. , Myhr R.
Published: April 2024

Dynamic reservoir rock typing is a critical yet infrequently explored aspect of CO2 storage, essential for evaluating flow characterization in dynamic reservoir modeling within aquifer reservoirs. This study introduces a new insight into the establishment of dynamic reservoir rock types for the supercritical CO2-brine system, leveraging relative permeability data and implementing it into 3D numerical reservoir simulation.

Our research draws on 22 sandstone core plugs obtained from potential CO2 storage aquifers in Alberta, Canada, encompassing measurements of relative permeabilities during primary drainage and secondary imbibition cycles. The rock typing methodology employed incorporates pore geometry and pore structure (PGS), in conjunction with the True Effective Mobility (TEM) function, to comprehensively characterize multi-phase fluid flow properties in rocks. Subsequently, we visualize the outcomes of the rock typing process through 1D and 3D model representations, including the simulation of flow characteristics through compositional numerical modeling for geological CO2 storage.

As a result, four rock groups were established based on pore geometry and pore structure relationships in the studied samples. The critical findings are that the obtained results demonstrate clear groupings of similar TEM-function curves based on relative permeabilities of both brine and CO2, observed in both drainage and imbibition experiments. Averaged relative permeability curves were derived from the TEM-function and subsequently converted them into conventional relative permeability values for each rock type. Notably, 3D numerical simulations of flow dynamics unveiled unique and contrasting multi-phase fluid behavior within each rock group, particularly evident in saturation profiles over time. Furthermore, we evaluated the correlation between residual CO2 trapping and irreducible water saturation within the rock samples. Our findings suggest an inversely proportional relationship, indicating that higher irreducible water saturation leads to lower residual CO2 trapping.

As a novelty, combining PGS rock typing and TEM-function analysis facilitated the effective and efficient grouping of capillary pressure and relative permeability data, ensuring high consistency and minimized overlap in each rock type. Moreover, this approach offers an alternative solution for averaging relative permeability data within each rock type that can greatly reduce the uncertainty of defining relative permeability input and accelerate the process of dynamic reservoir modeling.
Building Trust in AI/ML Solutions: Key Factors for Successful Adoption in Drilling Optimization and Hazard Prevention
Authors: Schaefer S. , Revheim O.
Published: April 2024

The use of AI/ML technologies has provided breakthrough performance in automated predictive data analytics. With the increasing amount of data available during drilling operations, data driven AI/ML solutions lay out the future of current technologies for drilling optimization and hazard prevention. Fast adoption and appeal of these technologies to the industry could be explained by a few reasons: AI/ML enables digital transformation by using only real-time data without extensive requirements for contextual data so that engineering and data input processes can be fully automated;AI/ML solutions predict outputs based on the data trends allowing to solve problems where conventional models are hard to implement or are not sensitive enough to identify subtle anomalies;Targeted solutions address specific problems and become more applicable in the modern digital ecosystem,Due to previous reasons, such technologies are easier to implement and to scale up in the operational environment.

Successful adoption of AI/ML technologies lies in its validation and trust in the operational environment. Based on the project experience from various parts of the world, prerequisites for building trust have proven to be: high performance AL/ML technology;matured IT infrastructure with relevant support services to enable digital transformation;monitoring specialists in an established RTOC or rigsite team to validate solution decisions;good communication protocol and established responsibilities of the RTOC and rig team to validate the impact of the predictions and to apply for operations.

The success factors are consequently related to technology, infrastructure and "soft" aspects like work processes, team interactions and defined roles and responsibilities. Each of these areas will be addressed individually.
Performance and Modelling of a Density-Based Autonomous Inflow Control Device
Authors: Corona G. , Greci S. , Novelen R. , Warren C. , Langaas K.
Published: April 2024

Autonomous inflow control devices (AICDs) are used to improve reservoir influx and restrict unwanted fluids for higher oil recovery. A new AICD has been developed to restrict water based on the density of downhole fluids. This is particularly useful in reservoirs where the oil and water have similar viscosities. This paper presents full scale flow performance results at reservoir conditions and modelling to predict the impact of this Density-AICD in well completions under various scenarios.

The Density-AICD is equipped with a novel Centrifugal Fluid Selector that magnifies subtle differences in buoyant forces and makes it orientation independent. The switching mechanism will autonomously adjust the choke to reduce the flow rate when changing from oil to water. A full-scale flow loop test was completed using a Norwegian oil crude sample and water at downhole conditions. The experiments evaluated the performance of the tool over different pressure differentials with single phase and multiphase fluid mixtures. To explore the impact of this device, a simple conceptual reservoir model was constructed.

The flow performance test results demonstrated that the tool produces oil at high flow rates and chokes water effectively. Two different nozzles sizes were tested proving the expected ratios of oil to water. It also confirmed the switching capability of the device to independently open and close based on the density contrast of the fluids. The multiphase testing showed the reversibility of the device and the water cut level necessary to close and open the device across a range of differential pressures.

The approximate behavior of this Density-AICD was implemented in an industry standard reservoir simulator. The model used realistic reservoir conditions from a Norwegian sub-sea development. The study shows that the Density-AICD can significantly decrease tail-end water production and further increase oil recovery across most reservoir scenarios.

The Density-AICD's self-adjusting feature simplifies and addresses uncertainties in the reservoir to restrict water, enhance oil recovery, optimize reservoir management, reduce water treatment costs at surface and contribute to reduced carbon intensity.

This paper publishes new flow performance test data for the Density-AICD using a Norwegian crude oil sample at representative downhole conditions. The conceptual reservoir modelling indicates a possible step-change autonomous inflow control technology that enables more carbon-efficient reservoir management.
EOS Modelling of Export Sales Products & Water of Condensation Bringing Value to a Mature Gas Condensate Subsea Asset in the North Sea
Authors: Mogbo Obuekwe
Published: April 2024

TotalEnergies operates four offshore fields in the West of Shetlands area. Traditionally, Black-oil based reservoir models linked to an integrated asset model (IAM) were used capture the reservoir-to-plant fluid dynamics in these mature, turbiditic, gas condensate fields produced through a long subsea tieback.

The project incorporated EOS modeling into the operational framework to engender multifaceted advantages. Firstly, it empowered asset managers with accurate predictions of water of condensation behavior, enabling optimized flow assurance measures to mitigate issues like hydrate formation and pipeline plugging. Secondly, the refined modelling of hydrocarbon composition improved the capacity of tailoring production techniques to maximize yields of Export Sales products. Additionally, compositional simulation methods enabled prediction of the backout impact on both a volume and energy basis, allowing finer computation of the economic impact of third-party tie-ins.

The EOS lumping was designed to replicate major sales products (LPG, Condensate etc.) and was further modified, introducing water as an EOS component in order to capture the water condensation phenomenon. Water vapor was introduced into the initialized reservoir Fluid Column without compromising earlier fluid properties matched to PVT lab observations, allowing wells to produce this water element as part of the liquid stream after condensation. A plant pseudo process was established from Process Flow Diagrams and historical plant pressure and temperature conditions were applied via periodic updates in the IAM.

The compositional simulation results were positive with excellent match to historical export sales products and water cut. Increased confidence in ability to predict condensed water allowed significant volume and cost optimization in MEG injected for Subsea Network Hydrate prevention. The enhanced accuracy in forecasting energy basis backouts vastly improved the economic assessments of third-party tie-ins.

In conclusion, this project underscored the value proposition of incorporating EOS modeling in a mature Gas Condensate subsea asset in the North Sea. By harnessing the power of EOS models, a mature asset could be managed with heightened precision, optimizing export sales products, ensuring flow assurance, and enhancing the modelling of economic returns. Such an enhanced physics approach modelling clearly contributes to extending the lifespan and economic viability of the aging offshore assets and also assists in making potential future investment decisions.
Revealing Microstructure and Enduring Properties of Settled Barite Extracted from an Offshore Well Two Decades Later – Well Abandonment and Slot-Recovery
Authors: Yousuf H. B. , Khalifeh M. M. , Saasen A. , Godøy R. , Karlsen H. C. , Naumann M. , Ibragimova Z. , Kruber C. , van Noort R.
Published: April 2024

Barite settling in the annulus behind the casing is an undesired yet common occurrence during the life of a well. Over time, the drilling fluid left in the annulus settles, leaving behind solidified barite that can hinder slot recovery and Plug & Abandonment (P&A) operations by impeding the cut and pull process. During a P&A operation, Equinor acquired settled barite samples from a North Sea well where the casing was held back by these weighting agents, introducing overpull and prolonging the cut and pull operation. A laboratory analysis program that included determination of the particle size distribution, electrokinetic potential of particles (zeta potential), crystallography (XRD), chemical composition (XRF), thermogravimetry analysis (TGA), and microstructure (QEMSCAN) was carried out on these settled barite samples to understand their properties. The results of this study provided valuable insights into the composition and characteristics of the settled material in the annulus. The solidified barite within the annular space exhibited no signs of chemical reactions. XRD analysis confirmed the barite to be the sole predominant solid in the solidified material, aligning with expectations. However, additional analytical techniques, including XRF, QEMSCAN, and Energy-Dispersive X-ray Spectroscopy (EDX) detected interconnections between barite particles, primarily facilitated by iron or quartz particles. Additionally, trace amounts of calcite, iron-oxide, SiO2, and mixed compositions involving Si, S, Fe, Ba, O, and Cl were identified within the solidified material. Scanning Electron Microscopy (SEM) results indicated that the particles exhibit strong compaction characteristics but lacked cementation, retaining some porosity. Notably, the absence of bentonite or other clays was consistently observed in all analyses. Furthermore, the zeta potential measurements of the samples showed a more stable response than the API barite. This study highlights the process of solidification observed in settled barite, suggesting that factors other than chemical reactions may be responsible for this phenomenon. The potential mechanisms contributing to solidification include physical aggregation, compaction, and alterations in surface charge under downhole conditions. This enhanced understanding of the solidification process will contribute to the development of solutions for efficient casing removal and even the utilization of settled barite as a barrier material.
A Digital Twin for Reservoir Simulation
Authors: Keilegavlen E. , Fonn E. , Johannessen K. , Tegnander T. , Eikehaug K. , Both J. W. , Fernø M. A. , Kvamsdal T. , Rasheed A. , Eigestad G. T. , Nordbotten J. M.
Published: April 2024

We have developed a physical room-scale porous media flow rig for operating, measuring, and visualizing reservoir flows in real time – the FluidFlower. The flow rig scale is large enough to achieve true multiphase flow effects (including phase mixture, gravity segregation and geological heterogeneities), while small enough to work on weekly time-scales, and allow for repeatable experiments.

Mirroring the FluidFlower, we have constructed a prototype of a digital twin for porous media flow – the PoroTwin. Essentially, we demonstrate that it is possible to achieve real-time transmissions of laboratory data from the FluidFlower to a cloud-based simulation- and machine learning environment, and complete the loop with applying optimal control algorithms to steer the experiment.

As part of the proof of concept, we also demonstrate that the machine learning environment can identify, and learn to correct for, incomplete physical descriptions within a reservoir simulator. The PoroTwin thus shows the potential of a fully integrated experimental and automated learning environment.
Enhanced Certainty of the Formation Boundary Location Through Novel LWD Interpretation Methods. Case Study from the Norwegian Continental Shelf
Authors: Nemushchenko D. A. , Krueger M. C. , Zaputlyaeva A.
Published: April 2024

Advanced geosteering methods are essential for effectively targeting complex reservoirs. Over the past decade, the utilization of Deep and Ultra-Deep Azimuthal Resistivity (DAR and UDAR) tools has significantly improved the accuracy of well placement. In conventional reservoirs, resistivity inversion results, calculated using DAR and UDAR data, have become a primary guiding factor for geosteering. Alternative methods such as stratigraphy-based geosteering are less commonly employed in conventional formations though they could provide low uncertainty interpretations of the well path location within the reservoir. This study demonstrates how stratigraphy-based correlation gives a thorough understanding of the lateral well location within a carbonate reservoir on the Norwegian Continental Shelf. Uncertainty in the interpretation is greatly reduced by the integration of geosteering interpretation methods, including inversion calculation from UDAR tools, and image log interpretation.
Achieving Water Shut Off (WSO) with Targeted Epoxy Injections in Open Hole Gravel Pack Subsea Gas Wells
Authors: Lootz K. , Sirevaag B. , Ciccarelli J. R. , Mirza D. , Ringe T. , Oddie K.
Published: April 2024

Methodology and results are presented for successful and unsuccessful zonal Water Shut Off (WSO) interventions for offshore subsea gas wells located in the Norwegian Sea with open hole gravel pack completions that do not have existing packers or other mechanical zonal isolation options outside of the screens. The field is produced by depletion and the water production needed to be halted to ensure the longevity of wells.

The operations were performed from a Riserless Light Well Intervention (RLWI) vessel. First the water producing zones were identified. Water was then isolated by perforating at a 3.5 m section of blank pipe between two production screens and epoxy was injected into the gravel pack to create an annular flow restriction between the formation and sand screen by use of an epoxy injection tool. Shortly after the epoxy cured, a bridge plug was installed on the inside of the screens at the same depth. The WSO depth had to coincide with a local reservoir pressure boundary such as a sealing shale.

The same WSO method was performed on three wells. These wells have produced between 1 and 4 million m3/d of gas, one well had water production from the first day of production and the other wells developed water after months of production resulting in water rates between 120-1000m3/d. Each well had varying levels of gravel pack quality between 50-100% and varying length/tortuosity; these factors became crucial to the success of the operation. All three wells will be discussed where 2 out of 3 of the wells had successful WSO with formation water eliminated from production. The interventions have extended the lives of all the wells by reducing fluid column weight (due to declining pressure) and boosting gas production given the water handling limitations of the facility. Two of the wells had such high water production and crossflow when shut in that they were expected to die within weeks without any further action by these interventions (refer to Table 1). Continued water production also decreased the viability of other wells in the same flow line, due to the subsea layout and associated limitation on maximum water handling capacity due to water increase.

The operation was performed on 3 wells and the learnings and planning was taken from one well onto the next, improving with each iteration. The gathering and use of all subsurface and well/intervention disciplines with all available data to decide on best shut-off locations was key to the success of these interventions.
A New Automated Workflow for Well Monitoring Using Permanent Pressure and Rate Measurements
Authors: Shchipanov A. , Cui B. , Starikov V. , Muradov K. , Khrulenko A. , Zhang N. , Demyanov V.
Published: April 2024

Pressure and rate measurements are essential for the well and reservoir surveillance workflows used in petroleum, geological carbon storage, and geothermal industries. Such well monitoring data are now analyzed in manual, semi-automated and automated modes or in a combination. Automated workflows are widely adopted by the industry nowadays, enabling most efficient knowledge extraction from the data both already accumulated and being received in real-time.

The paper describes a new integrated workflow for automated well monitoring using pressure and rate measurements obtained with permanent gauges and flowmeters. The workflow is based on time-lapse Pressure Transient Analysis (PTA) and integrates the following components: virtual flow-metering, transient identification, feature extraction and pattern recognition in transient pressure responses, and assessment of well performance based on PTA-metrics. The methodology behind the workflow combines different physics-informed and data-driven methods described in the paper. Application of the workflow is illustrated on a field case example from the Norwegian Continental Shelf, where changes in the well, reservoir, and well-reservoir connection performances are automatically monitored during its three-year long injection history. Reliability and accuracy of the automated monitoring results are verified via comparison with the conventional model-based time-lapse PTA.

The automated workflow may be used for a variety of use cases. Being applied to the well history, the workflow enables establishing a historical performance profile and identifying trends and issues in the past. In everyday well monitoring, it may be employed to detect well performance issues early and indicate their possible reasons. Further, it may provide valuable input for in-depth model-based analysis and other reservoir engineering studies. Using the workflow unlocks knowledge hidden in abundant well-monitoring datasets available at operating companies and empowers reservoir engineers to instantly assess well and reservoir performances, understand their interconnectivity, and make prompt, informed decisions.
Impact of Intermittency on Salt Precipitation During CO2 Injection
Authors: Landa-Marbán D. , Zamani N. , Sandve T. H. , Gasda S. E.
Published: April 2024

We study the effect of different parameters such as injection rate, permeability, and capillary pressure (grouped as capillary number) on salt precipitation and CO2 propagation within the reservoir during intermittent injections. Recent development in the open-source OPM Flow simulator allows to include salt precipitation in CO2 storage simulations. The logarithmic extension for the capillary pressure curve on the dry-out zone and the Killough model for hysteresis are adopted. To set up the reservoir grid, schedule in the injection, and rock and fluid properties, we have developed an open-source framework in Python. Then, the setting of the simulations is achieved via a configuration file that allows not only for reproducibility of the presented results, but to simulate additional scenarios. We show a comparison to published results using TOUGH2, resulting in a good agreement between both simulators. We present a sensitivity study where the impact of different modelling choices (e.g., using a well model where the flow is uniform, or the flow adapts to the reservoir connectivity) is assessed. Simulations in heterogeneous layered systems using the facie properties from the recent SPE CSP11 problem definition is also presented. Finally, the effect of alternating injection/shut-in periods (1 year per period) is assessed for the same amount of injected CO2 in a time frame of eight years, resulting in 35 cases starting with CO2 injection. The results reveal that during more extended shut-in periods, salt precipitation is happening closer to the wellbore which confirms the effect of capillary pressure on imbibing more brine into the dried regions. Quantities such as solubility trapping and the integral of the well bottomhole pressure over time (used as an energy proxy) highly depend on the intermittency pattern.
On-Chip Power Source Using Optimized On-Chip Solar Cells Based on a Standard Bulk CMOS Process for Single-Chip Self-Powered Smart Microsensors
Authors: Liu Jingjing , Guan Jian , Mo Wenji , Xiong Bingjun , Sun Kangkang , Yan Feng , Li Zhipeng , Choubey Bhaskar
Published: April 2024

Enhancing the photoelectric conversion efficiency of on-chip solar cells is important for the realization of self-powered smart microsensors. The surface electrode models for the on-chip solar cell based on CMOS process is constructed. It is verified by simulations and measurements that square ring electrode (RE) and center electrode (CE) don’t cause significant differences in the internal resistance of solar cells. Adopting the CEs instead of the REs can significantly reduce the shadowing effect of surface electrodes. To solve the problem of light blockage caused by the metal interconnections in the segmented solar cells, highly doped regions are used to replace some of the metal interconnections. A 0.01mm2 segmented triple-well on-chip solar cells with the CEs and highly doped region as interconnection is fabricated using a standard 0.18μm CMOS process. Measurement results show a 25.79% photoelectric conversion efficiency under solar simulator illuminations and has a 17.49% improvement compared to the conventional design. Utilizing the proposed solar cells, an on-chip energy harvesting power source has been realized, achieving a maximum conversion efficiency of 10.20% from incident solar power to voltage output power. Despite variations in illumination and load, this power source is able to maintain a relatively stable output voltage of 1V.
Experimental Study on Hydraulic Fracturing with Acoustic Emission under Tri-Axial Stress Conditions
Authors: Esmaeilzadeh Ali , Nikkhah Majid
Published: April 2024

Hydraulic fracturing method is known as one of the most important stimulation methods, especially in unconventional gas and petroleum reservoirs. Acoustic emission (AE) is a popular technique to monitor the process of geomaterial failure during hydraulic fracturing. In the present paper, AE monitoring has been employed for a better understanding of the mechanism of crack and fracture generation in experimental hydraulic fracturing. Concrete blocks were subjected to true tri-axial stress conditions while AE was synchronously recorded during fluid injection. The experiments have been performed on the specimens under different loading stresses in order to investigate the effect of the applied pre-stress combinations. Based on the results, as the minimum horizontal stress increases, the fracturing pressure increases; moreover, when the maximum horizontal stress and vertical stress increase, the fracturing pressure shows an increasing and decreasing trend. Observed fractures align with the direction of maximum horizontal stress under anisotropic conditions, while isotropic stress conditions result in randomly oriented radial fractures. Analysis of AE parameters provides valuable insights, correlating with the physical fractures and cracks behavior and demonstrating the potential of AE monitoring as a predictive tool for hydraulic fracturing behavior.