bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing Risk In Long-term CO2 Storage Under Uncertainty Via Survival Analysis-based Surrogates
Authors: Gurwicz A. , Chen J. , Gutman D. H. , Gildin E.
Published: September 2024

Reservoir simulations for CO2 sequestration are computationally expensive because they run for centuries or millennia. Shortened, cheaper simulation timelines prevent the observation of gas leakages that might occur over a longer duration. In the statistics community, the exclusion of these leakages is called censorship. We propose a fast simulator surrogate that captures these potentially unobservable long-term risks. The crux of our approach is survival analysis, a branch of statistics tailored to handle censored data.

Our proposed framework begins by sampling scenarios with varied injection and production rates from a number of geological and flow uncertainty realizations. The potentially censored time to gas leakage is recorded for each simulated scenario. We then leverage varied survival analysis methods, ranging from Kaplan-Meier to Random Survival Forests, to create a computationally cheap, and highly interpretable, simulator surrogate. The surrogate can predict the risk of CO2 leakage in new scenarios for significantly shorter simulations than usual in carbon sequestration studies, or no new simulations at all. This eases the computational burden of centuries-long, expensive simulations.

In order to validate our methodology, we constructed an exploratory case study with a shortened monitoring window. The proposed framework is implemented within a compositional simulation model where CO2 is injected into a saline aquifer. To assess the risk of leakage and caprock integrity, we simulated scenarios where we inject CO2 for permanent storage using 4 injector wells, while 5 producer wells are used for pressure maintenance. This model is run for 2000 days under varied permeability realizations to monitor CO2 breakthrough from the production wells. We then infer the occurrence of leakage in new scenarios and compare these results to full simulations via appropriate statistical metrics such as hypothesis testing, metrics tailored for the censored data context and usual prediction metrics. Initial results show that the proposed method predicts time to gas leakage with good accuracy without the need for any new simulations at all.

To the best of the authors’ knowledge, this is the first paper to approach the carbon storage optimization issue with survival analysis, a clear fit due to the presence of censored data coming from shortened simulations. Our unique, novel framework yields a simulator surrogate built with techniques never-before-seen in this context. Also, we fill the gap other approaches leave open by focusing on interpretability, a model quality that is paramount to decision-making under high uncertainty.
Improving Injectivity of CO2 in Saline Aquifers Through Chemical Additives
Authors: Penny G. S. , Bhagwat S. M , Shuchart C. E. , Gupta V. P.
Published: September 2024

A key metric for Carbon Capture and Storage (CCS) projects is the rate of injection of supercritical CO2 (sCO2) into the subsurface and maximizing injectivity will be important for success. The sCO2 injection rate into saline aquifers is limited by formation permeability and residual water saturation held in place by capillary pressure. The objective of this work is to explore the application of additives to lower the capillary pressure and the water saturation in the near wellbore region and thereby increase the relative permeability to sCO2. The intended benefit will be the ability to inject CO2 at higher rates while maintaining injection pressure below the desired level/fracture gradient or injecting at reduced pressures for the same rate.

Screening tests identified several types of additives (Nonionic/Cationic/Anionic/Amphoteric) that reduced brine-CO2 IFT by a factor of 10 from 20-40 mN/m to 2-4 mN/m at 1000-2000 psi and modified contact angle between CO2, brine and sandstone and dolomite cores from 20-25 degrees with 2% KCl alone to 30-40 degrees with additives at 1000 to 2000 psi. In brine displacement tests in 100 md cores, the best additives when used in a concentration range of 0.1% to 0.2% decreased the water saturation by 10-30% and reduced the displacement pressures by ∼50% over the control, resulting in proportional increase of the relative permeability to sCO2.

Well model results show that doubling the relative permeability in the near wellbore region can increase the CO2 injectivity by ∼20%. Recommendations on treatment volumes and procedures for candidate injection wells are provided. The approach presented here can provide a low-cost option to increase injectivity and other benefits including compensating for injection well downtime, providing higher injection rates in lower permeability rocks, and reducing injection pressures for safer and more cost-efficient operations.
Leveraging the Successful Integration of New Technologies to Solve the Key Drilling Challenge in the Eastern Amazon Region, Onshore Brazil
Authors: Quinones H. , Fernandes G. , Harada L. , Arruda G. , Carvalho J. , Chaves L. , Medeiros T. , Sena H. , S.A. Eneva , Schmidt A. , Aguiar R. , Bonilla P.
Published: September 2024

While conducting a wells campaign in North Brazil, the Operator suffered from significant non-productive time in the 8 ½-in deviated intermediate hole section, caused by BHA component failures while drilling hard diabase rock layers. Based on this outcome, an integrated Wells-Contractor team devised a plan to deploy new tools and technologies to efficiently drill the intermediate hole section in a single run, while controlling/mitigating shocks and vibrations, and preventing the re-occurrence of tool failures.

The strategy encompassed a detailed engineering analysis to refine the drilling system, integrating new tech PDC bits with geometrical cutting structure, an at-the-bit sensor module for downhole dynamics recording, a more robust mud motor technology, and an instrumented sub for real-time high-frequency downhole dynamics monitoring. For the initial implementation, a 3-well sequence was selected to map the frequency and magnitude of shocks and vibrations throughout the area of operations, thus contributing to an enhanced understanding of the phenomenon for further modeling of the most suitable drilling system to address the challenges of the section.

From the outset, well 4 had immediate significant improvement, with the first 8 ½-in BHA drilling through over 200 meters of diabase rock, more than doubling the meterage of the previously longest recorded run in well 3 and keeping drilling dynamics and tool integrity concerns in check. For the next 2 wells, the team further capitalized on the success of the refined BHA. Enticed by performance in well 4, the team started deepening the intermediate hole section to accommodate specific well design requirements. In well 6, the first BHA drilled through 328 meters of diabase rock, slightly surpassing the single BHA run meterage from the previous well and improving ROP. And in well 7, a single BHA drilled 1062 meters "shoe-to-shoe", including 300 meters of diabase rock, yielding a 33% ROP improvement against the average of the previous 2 best-performing wells.

On a shoe-to-shoe basis, the implementation yielded a significant 100% performance improvement in the overall section meterage rate compared to the previous campaign. This, in turn, contributed to a 50% reduction of the effective metric cost and reduced risk exposure by eliminating trips and associated BHA handling.

Well construction activities in North Brazil have their share of logistical and technical challenges. For the latter, the systematic integration of fit-for-purpose technologies and downhole measurements enabled the team to finally crack the code of hard rock drilling performance and dynamics in the Eastern Amazon basin, thus yielding great overall value for the project. As a result, the Operator plans to carry on the momentum and promote the next well design-related step changes across the region.
Deep Learning-Driven Acceleration of Stochastic Gradient Methods for Well Location Optimization Under Uncertainty
Authors: Eltahan Esmail , Sepehrnoori Kamy , Alpak Faruk Omer
Published: September 2024

We have developed the deep-learning-accelerated-gradient (DLAG) algorithm, a novel solution for well location optimization (WLO) problems that leverages data collected from the explored parameter space to accelerate optimization. During optimization, we collect data to train a deep neural network (NN), creating a map from well-location parameters to the objective function. We utilize the analytical gradient of the NN, resulting in an effective search direction while saving the computational cost associated with stochastic-gradient perturbations. Our method features a novel NN architecture, the Spatial Pairwise Interaction Network (SPINet) with independent and contextual neural pathways (NPs), designed to capture the primary well characteristics, and its complex interactions with the neighboring wells. For the contextual NP, we explore using the popular Attention mechanism and simpler mechanism with weight-sharing Multilayer Perceptron (MLP) layers.

To evaluate the architectures, we designed the Bird Ensemble (BE) test problem which resembles the structure of the WLO problem. The comparison between DL architectures reveals that the NP with a weight-sharing mechanism has superior performance in terms of MSE error and gradient accuracy. The weight-sharing structure allows the model to efficiently model relationships with shared parameters while maintaining invariance to input permutations. For the test function, utilizing DLAG drastically improves computational efficiency, reducing the number of function evaluations required to achieve the same level of optimization by one to two orders of magnitude. Following validation of this test problem, we successfully applied our method to optimizing locations of injection and production wells in the Egg reservoir model. To alleviate random artifacts and the inevitable possibility of encountering local minima, we conducted 20 iterations of the WLO problems both with and without DLAG. The results reveal that, on average, our DLAG optimization method is more efficient than the traditional approach. This efficiency gain is particularly impactful for real-world applications where project timelines are tightly constrained.
The U-Net Enhanced Graph Neural Network for Multiphase Flow Prediction: An Implication to Geological Carbon Sequestration
Authors: Tariq Zeeshan , Hoteit Hussein , Sun Shuyu , Abualsaud Moataz , He Xupeng , AlMajid Muhammad , Yan Bicheng
Published: September 2024

Monitoring CO2 pressure buildup and saturation plume movement throughout the operation of Geological Carbon Sequestration (GCS) projects is crucial for ensuring environmental safety. While the movement of CO2 plumes can be predicted with high-fidelity numerical simulations, these simulations are often computationally expensive. However, through training on readily available simulation datasets, recent advancements in data-driven models have made it possible to predict CO2 movement rapidly. In this study, we adopt the U-Net Enhanced Graph Convolutional Neural Network (U-GCN) to predict the spatial and temporal evolution of CO2 plume saturation and pressure buildup in a saline aquifer reservoir. Utilizing the U-Net architecture, which incorporates skip connections, enables U-GCN to capture high-level features and fine-grained details concurrently. First, we construct physics-based numerical simulation models that account for both GCS injection and post-injection periods. By employing Latin-Hypercube sampling, we generate a diverse range of reservoir and decision parameters, resulting in a comprehensive simulation database comprising 2000 simulation cases. We train and test the U-GCN model on a two-dimensional (2D) radial model to establish a U-GCN code benchmark. We utilize Mean Squared Error as the loss function throughout the U-GCN training process. The U-GCN model demonstrates robust performance on the radial model, achieving an R2 score of 0.993 for saturation prediction and an R2 of 0.989 for pressure buildup prediction based on the blind testing dataset. Notably, the Mean Absolute Percentage Error (MAPE) for all mappings consistently hovers around less than 5%, indicating the effectiveness of the trained models in predicting the temporal and spatial evolution of CO2 gas saturation. Moreover, the prediction CPU time for the DL models is significantly lower (0.02 seconds per case) than the physics-based reservoir simulator (on average, 45 to 60 minutes per case). This underscores the capability of the proposed method to provide predictions as accurate as physics-based simulations while reducing substantial computational costs.
Determination of Dispersion Coefficient of Solvent in Heavy Oil/Bitumen Under Reservoir Conditions
Authors: Zhao Wenyu , Yang Shikai , Yang Daoyong
Published: September 2024

In this work, the dispersion coefficient of solvent in heavy oil/bitumen is innovatively determined by employing an inverse approach to accurately reproduce the measured temperature, pressure, and production profiles in a heated vapour extraction (H-VAPEX) process, thereby significantly advancing our insights into the dispersion physics under reservoir conditions. Monitoring and surveillance data, including temperature profiles, solvent injection rates, and fluid production rates, from a large three-dimensional (3D) physical experiment for an H-VAPEX test have been meticulously collected, analyzed, and processed. More specifically, the evolving geometries of the solvent chamber have been precisely delineated by integrating the advection-dispersion equation (ADE) with a modified Peng-Robinson equation of state (PR EOS), where the solvent chamber interface (SCI) is theoretically formulated as a function of temperature and solvent concentration gradient. As dispersion physics significantly impacts the SCI advancement, the dispersion coefficient in the presence of porous media can be inversely determined by assimilating the measured and calculated morphological contours of the solvent chamber. Such an integrated model has been rigorously validated with the measured temperature profile of a steam-assisted gravity drainage (SAGD) process from a one-dimensional (1D) physical model collected from literature, and subsequently extended to a large-scale 3D application. In addition, sensitivity analysis has been performed to analyze and identify the key parameters dominating the H-VAPEX performance. Not only does the injection velocity of solvent and its concentration gradient at the SCI exert a significant influence on its anisotropic dispersion in various spatial dimensions, but also the injection velocity dictates the solvent propagation over time. A higher injection velocity is found to accelerate the expansion of solvent chamber, thereby promoting solvent dispersion and resulting in a more pronounced solvent concentration gradient beyond the SCI. Under reservoir conditions and with high injection velocities, the dispersion coefficient is found to substantially exceed the diffusion coefficient by several orders of magnitude. Nevertheless, a large reduction in the viscosity of heavy oil/bitumen saturated with solvent results in a great increase in fluid mobility, facilitating the solvent injection as well as SCI propagation. Through the repeatable and consistent H-VAPEX experiments within the large 3D physical model, this systematic and robust method enables us, for the first time, to not only inversely determine the dispersion coefficient of solvent under reservoir conditions, but also to accurately evaluate and optimize the growth and propagation dynamics of the solvent chamber.
The Role of Petroleum Engineers in the Energy Transition: Developing Workforce through an Introductory Camp
Authors: Enab Khaled , Tashtoush Tariq , Khasawneh Mahmoud , Elmasry Youssef , Pena Julian
Published: September 2024

This study highlights the benefits of a hands-on four-day camp on the recruitment of high school students for petroleum engineering programs. The majority of participating students in this camp were Hispanic students who are underrepresented in engineering professions in general, including the oil and gas industry, despite being well represented in the labor technician side of the discipline. The introductory camp sought to introduce students to the role of petroleum engineers in the energy sector and Net Zero Carbon vision, in addition to stimulating their interest in petroleum engineering as a potential career path. The four-day camp was designed for a total of 8 contact hours per day that were scheduled into two four-hour segments per day. The first 4-hour segment focused on teaching students the fundamentals of petroleum engineering, and the second 4-hour segment focused on teaching students additive manufacturing and its role in the design and modification of tools. Throughout the third and fourth day of the camp, students completed a team-based project that connected concepts from petroleum engineering and additive manufacturing under the context of innovating drilling capabilities to address future energy demand. Twenty-eight students from three high schools participated in the short camp (27 Hispanic and 11 female). Participants completed pre-and post-surveys to evaluate the impact of the camp on their understanding of the role of petroleum engineers in the energy transition and decarbonization efforts, as well as their interest in joining the oil and gas industry. The outcomes reflect the effectiveness of the camp in clarifying the role and work of petroleum engineers in the oil and gas sector and their importance for future energy sustainability. This approach attempts to clarify misconceptions about petroleum engineering, especially in relation to environmental concerns, while stoking interest in the discipline, to address workforce needs in the oil and gas sector. The novelty of this work is providing a hands-on, application-based approach to guide young students from underrepresented communities to a rewarding yet exciting career. Giving students more exposure and direct contact with mentors from technical disciplines is of paramount importance at a critical stage of selecting a lifelong career. The proposed approach can be adapted to undecided first-year college students. In this paper, we provide a detailed breakdown of the proposed camp that is easy to follow and replicate.

This camp was sponsored by The US Department of Education – Minority Science and Engineering Improvement Program – Grant # P120A230056.
Next-Gen Drilling: Harnessing Rig Action Plan Cloud-Edge Integration for Well Cycle Improvement and Enhanced Crew Dynamics
Authors: Zarate P. , Bott R. , Varghese B. , Borges T.
Published: September 2024

Improving well cycle time efficiency involves automating the benchmarking and replication of top-performer activities across a fleet, enhancing safety, reducing learning curves, and enabling real-time well performance monitoring for consistent operational excellence beyond on-bottom activities. The process involves consuming data from any digital well program, automatically converting it into a Driller's Roadmap for the digital execution of well-defined sequence of activities to eliminate guesswork and delays, to help with knowledge sharing minimizing the learning curve. Efficiently disseminating and implementing best practices, lessons learned and operational procedures by activity backed by data driven insights. Sending timely notifications and alerts to drillers and preparing crews to transition seamlessly into the next operation. The rig execution engine includes optimizing various drilling activities through specific recipe parameters, which will enable automation within a rig operating system all while providing visibility for all stakeholders.

As the industry progresses towards automating well design, a key innovation is the integration of these designs with a versatile rig execution engine, accessible at the edge and the cloud. An implementation conducted across 5 rigs working for a major operator in the Permian Basin demonstrated significant improvements in well cycle efficiency, highlighting the potential of this approach to transform well operations in the industry. The integration of a versatile rig execution engine with cloud-edge technology and enhanced crew dynamics resulted in observable well cycle time efficiencies, emphasizing the value of this innovative approach through flat time optimization. This integration between a rig plan and the rig execution engine increases procedural adherence through execution using a rig operating system that automates tasks according to the well program. Additionally, improves driller training and crew retention by providing timely, relevant information and resources, including multi-lingual support, thereby fostering greater inclusion in operations.
Field X Fail-Less Strategy: Journey Towards Esp Best in Class Performance and Achieving Record Low Subsurface Unscheduled Deferment
Authors: Al Shukaili Ghosin , Al Kindi Said , Al Riyami Said , Al Hatmi Fahad , Al Ajmi Ahmed , Al Riyami Yaqoob , Al Lawati Mehdi
Published: September 2024

The Field X is the largest cluster fields (23 Sub-Fields) in Petroleum Development Oman (PDO), producing the highest oil production and operating the highest number of well stock (over 2800 wells). Most of these fields are very mature with many old wells and aging facilities. Operations in Field X are very dynamic with more than 1600 well interventions yearly; critical activities to restore and optimize production and arrest the field's decline. Before 2020 Field X was experiencing significant subsurface unscheduled deferment (USSD) at 3.7% of total field potential and an artificial lift failure index of 28%, well above industry standards.

To address this issue, a dedicated workshop on artificial lift failures and USSD reduction was carried out with stakeholders, resulting in the development and implementation of the Field X artificial lift fail-less strategy. This strategy aims to convert frequently failing Beam Pump (BP) wells to Electrical Submersible Pump (ESP) and Long Stork Unite (LSU). Over 340 BP wells were converted to ESP and LSUs from 2020-2023.

Between 2020 and 2023 and as a result of Field X Fail-Less Strategy, there has been a dramatic improvement in Field X's performance. The overall artificial lift failure index has dropped from 28% to 18%. Moreover, Field X achieve a significant drop in ESP failure index from 14.4% in 2020 to 7.5% in 2023. This success exceeds the Global Top-Quartile Benchmark of 10.8% and field X is now considered as best in class performance in ESP. The subsurface unscheduled deferment rate of Field X has decreased from 3.7% to 2.1%, and some fields have achieved ZERO unscheduled subsurface deferment. The success is attributed to several innovative solutions, including the "Fast Track" conversion of frequent failure wells to a different type of artificial lift, converting non-frequent failure wells with good potential to electrical submersible pumps ESP, and aggressive installation of the Long Stork Unite (LSU) system. The implementation of various new technologies in artificial lift and optimizing well operating philosophies also contributed to the success.

Field X artificial lift strategy resulted in significant value realized for PDO and a positive impact on HSE. This paper will explain the journey towards achieving a lower Artificial lift failure index and how the Field X artificial lift strategy was developed and implemented.
Simulation and Prediction of CO2 Injection Characteristics in Carbonate Gas Reservoirs
Authors: Huang Yize , Li Xizhe , Elsworth Derek , Liu Xiaohua , Yu Pengliang , Qian Chao
Published: September 2024

Understanding the flow and transport characteristics and mechanisms of CO2 in water-saturated carbonate gas reservoirs is crucial for enhancing CH4 recovery and CO2 sequestration. We developed a multiphase multicomponent numerical model for gas and water phases, which considers the multiple transport mechanisms between CO2-CH4 and CO2-water and couples the chemical reactions of CO2 in water-saturated carbonate porous media. After validating the model with analytical solutions, we investigated the effects of CO2 injection density, in-situ CH4 density ratio, and initial fluid saturation on CO2 flow characteristics and porous media properties. The results indicate that the fluid density difference between different components controls their transport mechanisms: smaller density differences lead to higher concentration diffusion intensity between components, while larger density differences result in viscous flow with convective transport characteristics. The dissolution effect of CO2 on carbonate rock enhances the flow capacity of the porous medium for different components and provides greater storage space for CO2 sequestration. Fluid saturation is a key factor affecting the transport characteristics of CO2 in porous media. Due to the mutual solubility of CO2 and water, water accelerates the flow of CO2 in the porous medium, leading to premature CO2 breakthrough and negatively impacting CH4 recovery.
Experimental Investigation of How Supercritical CO2 Changes the Chemistry and Microstructure of Cements
Authors: Liu Y. , Hu K. , Kuru E. , Li H. , Lin Z. , Iremonger S. , DeBruijn G.
Published: September 2024

Carbon capture and storage (CCS) is a crucial technology for mitigating greenhouse gas emissions and combating climate change. However, the long-term integrity of well systems in CCS applications is a critical concern, as the supercritical CO2 (SC-CO2) can dissolve in water forming carbonic acid, which can chemically alter the cement. This study investigates the effects of SC-CO2 exposure on the integrity of cement blends used in well systems for CCS applications. Three cement blends were examined: neat class G based cement (G), neat class G based cement with fly ash (GF), and preliminary testing on a further optimized low Portland cement based system containing permeability reducing polymers (GFP). Samples were exposed to SC-CO2 for up to 56 days under 4,060 psi, 70°C, and the complex changes in their properties evaluated. A review of the various non-standard analytical techniques is described using the three cement systems and their associated changes as a case study for the unique insight and associated limitations that each of these analysis techniques can provide.

The results demonstrate that SC-CO2 exposure leads to dehydration, carbonation, and alteration of cement, affecting its pore structure, permeability, and mechanical properties. Blends GF and GFP exhibited improved resistance to SC-CO2-induced alteration compared to blend G, attributed to reduced starting portlandite (Ca(OH)2) content and less alteration of the pore size during exposure. Permeability tests using Nitrogen (N2) and water revealed that although SC-CO2 exposure increased permeability due to cement alteration and dehydration, the permeability remained low, in the tens of μD range. Post-CO2 exposure uniaxial compressive strength tests are difficult to interpret due to the non-uniform nature of the structure but short-term exposure to SC-CO2 enhanced the mechanical properties of cement due to CaCO3 precipitation, while prolonged exposure led to the carbonation of the outermost layer, creating a more complex failure mode. X-ray diffraction (XRD) analysis revealed that Calcium Silicate Hydrate (C-S-H) is more stable than the portlandite under SC-CO2 exposure, confirming the previously described results that minimizing portlandite content is an effective strategy for enhancing cement formulations for CCS applications.

The findings provide valuable insights for the development of robust well integrity systems in CCS applications. Future research should focus on optimizing cement blend compositions, exploring mitigation strategies, and establishing guidelines for the design and operation of well systems to ensure the safe and effective implementation of CCS technology.
Impact of Residual Fluid Composition and Injected Gas Purity on the Storage Capacity of the Depleted Shale Oil Reservoirs in the Presence of Diffusion and Sorption
Authors: Enab Khaled , Wang Zhicheng W.
Published: September 2024

Depleted shale oil reservoirs signify a forward-looking solution for CO2 storage that leverages existing resources — It presents an ideal opportunity for CO2 storage, primarily because extensive data collection from prior extraction activities offers a detailed understanding of their geological characteristics. Additionally, the massive number of depleted wells in the US shales makes it a viable option for carbon storage. Shales are known for their high organic content, which enhances CO2 adsorption into the geological formation, which in turn enhances the storage capacity and formation integrity. On the other hand, due to the high CO2 concentration gradient, the diffusion process plays a significant role in CO2 storage. The wealth of information collected throughout the lifetime of those wells, including the adsorption and diffusion rates, ensures precise planning and safe implementation of CO2 sequestration projects. This precise planning for carbon storage contributes to the environmental and energy sustainability goals. In this study, we evaluate the storage performance in shales to evaluate the impact of reservoir fluid composition, diffusion, and adsorption. Furthermore, the impact of the injection gas purity on storage capacity is evaluated. In this study, we assessed the CO2 storage in shales by conducting large-scale numerical simulations for a wide range of reservoir fluid compositions—retrograde-condensate, volatile-oil, black oil—and different purities of injected CO2 by considering diffusion and sorption.

The results showed that ignoring the adsorption and diffusion underestimated shale storage capacity while ignoring the diffusion only overestimated the storage capacity. These results emphasize the importance of including adsorption and diffusion when modeling CO2 storage. Furthermore, the purity of the injected CO2 is positively proportional to the storage capacity as the storage capacity is reduced when the purity is reduced due to the increasing competition of the impurities to be adsorbed into the shales.
Validation of the First Industrial High-Power Laser Perforation Deployment
Authors: Batarseh Sameeh I. , Alerigi Damian P. San Roman , Marshall Scott , BuHassan Shaker
Published: September 2024

This paper presents the methods for validating the first successful industrial and field deployment of a high-power laser (HPL) perforating tool. The technology is able to perforate wells without damaging the surrounding substrate's integrity and improving the formation's flow-related properties. Validating the successful operation and the tool's performance is comprehensive and rigorous, relying on qualitative assessments of the data collected from built-in sensors and conventional logs.

The accomplishment of the first deployment is a testament to the meticulous research process that has led to the development of the first high-power laser field system. The tool integrates optical, mechanical, and sensing components. The optical and opto-mechanical assembly steers the beam to a target at any azimuthal angle. The sensor array collects data about pressure, temperature, and luminosity that are crucial to monitoring the operation, ensuring the tool's correct functionality, and validating the process. The high-power laser generator is located at the surface and transmitted via fiber-optic cable to the tool in the downhole.

The first industrial high-power laser perforation technology was successfully tested and deployed in a cased well; lab and field data proved that the technology could effectively perforate all types of rocks, demonstrating its versatility and adaptability. The tool created several perforations and slots through casing, cement, and formation. The validation of the operation is critical to evaluate the technology's performance and to compare it with current practices (e.g., shaped charges). This assessment used data from routine field measurements and the tool's onboard sensors, including a downhole camera, injectivity test, and sonic and caliper logs. The sensors measured temperature, pressure, and backscattered luminosity. The last is essential to characterize the laser-rock interaction since it is correlated with the effectiveness of the removal process. Temperature readings inside the tool provide information about the health of the optical components. In contrast, the outer temperature and pressure sensors are proxies of the wellbore's flowing dynamics and can help confirm the successful perforation.

This HPL perforating method is an innovative and non-explosive alternative to conventional shaped-charge guns. It is cost-effective and enables precise beam control, targeting specific zones and creating enhanced perforation tunnels uninfluenced by the formation's state of stress.
Applying Boundary Conditions for Large Aquifer Models in Geological CO2 Storage Projects; Why and How?
Authors: Ghomian Yousef , Bennett Matthew , Skrivanos Chris , Marchiano Iryna , Haynes Andrew K.
Published: September 2024

Around the world many projects are being considered for the geological storage of CO2 in large saline aquifers. The aerial extent of pressure and saturation plume forecasts, known as Area of Review (AOR) in the United States, can have a significant impact on the monitoring costs and liabilities associated with existing legacy wells within the vicinity of the carbon storage projects. Appropriately modeling how aquifer systems react to large scale CO2 injection has relevance to potential geomechanical integrity, fault reactivation, seismicity, and impact on underground sources of drinking water (USDW). Static and dynamic models are generally built over a smaller portion of large saline aquifer system. This is typically tied to the balance between model size, fidelity, and computational expenses. Assigning the appropriate boundary conditions to replicate the true extent of the aquifer is investigated in this study. Scoping the aquifer's size, geology, and properties is an important step that allows for the quantification of the appropriate pore volume beyond the area of the numerical model. Results from our study indicate that using pore volume (PV) multipliers is a reasonable approach if accompanied by transmissibility reduction at the interface between the PV modifications and the reservoir model. A simple model was developed to create the true solution for comparison and verification purposes.

Based on the results of this study, we find that using a combination of PV and transmissibility multipliers replicates the true solution more accurately. We demonstrate that large PV adjustments without transmissibility reductions overestimates the true aquifer strength, resulting in overall lower pressures due to large-scale CO2 injection. We demonstrate a systematic methodology for calculating the simultaneous PV increase and transmissibility reduction that scales easily for a wide range of scenarios.

Further investigation shows that as the PV multiplier grows large, the approach of reducing the transmissibility with a single multiplier starts to choke off the large added pore volume. This study proposes an innovative approach which demonstrates that applying a gradually increasing PV multiplier combined with gradually reducing transmissibility is a more accurate representation of the true aquifer system when compared to a single large PV multiplier and a single transmissibility reduction. The proposed approach was applied to large 3-D models and significant impact was observed in pressure diffusion front shape and extent when compared to more simplified approaches.

Assignment of boundary conditions for a variety of different scenarios were also investigated in this study. It was shown that this novel approach will be the most accurate method of applying appropriate boundary conditions in obtaining reliable AOR forecasts when modeling large aquifer systems for CO2 storage projects.
Enhanced Hydrogen Generation from Olivine Sand
Authors: Vega B. , Ross C.M. , Kim T. , Frouté L. , Kovscek A.
Published: September 2024

An experimental setup was created to study serpentinization of an olivine sand sample and study hydrogen production. Inspired by the so-called "white" hydrogen sources, enhanced serpentinization of olivine represents an attractive opportunity to produce hydrogen from geological resources. Literature offers examples of experimental conditions that achieve hydrogen production at laboratory scale with different degrees of success. We made a preliminary selection of experimental parameters and assembled a setup capable of replicating conditions suitable for hydrogen production from enhanced olivine serpentinization. Commercially available olivine sand was selected with a grain size range of 250 to 355 µm for the experiment. The sample was subjected to 18 days of high pH brine exposure via continuous inflow, while inside a reactor heated by a furnace. The operating temperature range inside the reactor was between 80 and 245°C. The produced gas phase was separated, periodically collected, and analyzed via gas chromatography calibrated for hydrogen. Analysis of the produced gas showed hydrogen concentrations of 9 and 13% by mole. The precursor olivine sand was characterized in pre- and post-reaction states using scanning electron microscopy and analytical techniques (x-ray diffraction and x-ray fluorescence). A significant amount (14 wt% of the reacted sample) of serpentine precipitate was observed, coating the grains. The analysis suggests that the serpentinization reaction was maintained by the large grain size and the development of accessible porosity between the grains and grain-coating serpentine precipitates. Minor amounts of carbonate, magnetite, and variable composition (Ca-Fe-Mg-Ti) silicate precipitates were also observed. These preliminary results suggest that it is possible to enhance, at laboratory scale, serpentinization of olivine and generate hydrogen with significant H2 compositional yields. Our findings are supported by produced gas analysis and evidence collected on the reacted sample.
Production Decline Analysis of Horizontal Shale Wells in Presence of Stress-Controlled Flow Rates
Authors: Wheeler Austin W. , Aldana-Gallego Ivan C. , Akkutlu I. Yucel
Published: September 2024

Decline curve analysis (DCA) methods curve-fit historical production data of a well and forecast the recoverable reserve. However, these methods do not consider the underlying physics of production and therefore they are not used as diagnostic tools. In this paper a new physics-based DCA method is introduced for reserve estimation and as a diagnostic tool for unconventional wells with long lateral length and with a great many hydraulic fractures. The method considers that the shale wells produce at flow rates that are influenced by in situ stresses. Namely, the reservoir matrix delivers the fluids to the fractures in an effective stress field, and the hydraulic fractures deliver the fluids to the well under fracture closure stress. The stresses change dynamically during the production due to withdrawal of the fluids and pressure depletion. We propose the production decline due to stresses can be captured by considering both the matrix and the fracture have stress-dependent permeability. We consider exponential function to capture this decline with a time-dependent exponent a representing the overall permeability loss ratio. The ratio changes over the production time as a function of the geo-mechanical properties and the changing stresses. It is the new DCA parameter to be determined by curve-fitting the production data. We used fourteen Marcellus shale gas wells, twenty shale oil wells (including wells from Permian, Eagle Ford, Anadarko basins) and utilized MS-Excel solver to demonstrate the application of the new stress-controlled DCA. On average a 30% loss in Marcellus gas reserve is estimated due to stress-controlled production rate decline. The shale oil wells analyzed also show strong dependence to stresses. The new approach could help operators identify the shale wells producing with flow rates under significant stress effects but also help improve the reserve estimation by adding new relevant physics and, hence, reducing uncertainty. In addition, it may be considered as a simple proxy model to use along with the complex reservoir flow simulation modeling approaches or as alternative to simulation in the absence of data needed for reservoir modeling.
Material Selection, Compound Development Criteria and Elastomer Grade Optimization of Packer Element in CCUS Storage Well
Authors: Meng S. , Ren J. X. , Cheng P. , Li J. Y. , Qi C.Y.
Published: September 2024

CO2 storage is an integral part of CCUS, whereas packer element for CO2 storage well is an essential component that must perform reliably for decades. The key to superior reliability is a high performing elastomer. This paper intends to compare the elastomers in oilfield applications and recommend the best fit for packer element under field application. It identifies the major packer element failure mode, establishes the compound development criteria, and ultimately guides elastomer grade optimization.

The sealing performance of packer element in CO2 well was reviewed, and the major failure modes identified. The compound development criteria were established successively. The characteristics of the elastomers in oilfield were evaluated for packer element application in CO2 storage well. The best elastomer was selected based on the criteria established. Special attention was paid to the optimal selection of elastomer grades. The effect of macromolecular structures on the compatibility of CO2 was studied from the thermodynamics aspect and later investigated by the immersion testing of different elastomer in supercritical CO2 and brine mixture under downhole temperature and pressure.

The major cause of long-term failure of packer element has been found to be the compatibility between CO2 and elastomer. The major cause of short-term failure is the rapid gas decompression (RGD) damage. Hence, the compound development criteria are set to be CO2 compatibility, RGD resistance, low temperature flexibility and chemical resistance. Upon the characteristics comparison of ethylene propylene diene monomer rubber (EPDM), Nitrile Butadiene Rubber (NBR), Hydrogenated Nitrile Butadiene Rubber (HNBR), and Fluroelastomer (FKM), HNBR is the best candidate meeting all criteria under field condition. To maximize the material life of HNBR packer element, one needs to design the formulation with a HNBR grade that is thermodynamically incompatible with supercritical CO2. However, selecting an optimal HNBR grade is a debatable subject in the industry. Some prefer low ACN grades while many others prefer high ACN ones. The dispute on HNBR grade selection may be reconciled by our immersion testing results indicating that as the ACN content of HNBR goes up, the material suffers more mass loss as well as volume increases and tends to have more bubbles and blisters on surface. On the other side, the higher ACN grades retain the mechanical properties better than the lower ACN ones.

High performing elastomer for packer element can significantly improve the reliability and reduce the operation cost of CO2 storage wells and overall CCUS value chain. Selecting the best elastomer is essential to the packer element performance. This paper is one of the first attempts to address the issues of elastomer selection, compound development criteria, and elastomer grade optimization. Our results may resolve a long-standing dispute in the industry.
Digital Twin for Optimization of Wells with Intelligent Completion
Authors: Srivastava S. , Gambaretto A. , Shresth K.
Published: September 2024

The evaluation and optimization of wells with intelligent completion, whether multilayer or multilateral, requires a deep understanding of inflow characteristics at each inflow control valve (ICV). Zonal testing is crucial for gathering fluids and reservoir data; nonetheless, it leads to production deferment, which is undesirable by most operators.

In this paper, we present how a wells’ digital twins on an edge Internet of Things (IoT) device will provide real-time virtual measurements as well as ICV optimization opportunities to maximize oil production. We present a digital twin solution for a synthetic well equipped with an electric submersible pump (ESP) and two ICVs. The digital twin system is composed of two primary components together called the estimator. One aspect is a physics-based well model that accurately calculates pressure losses and flow characteristics, and the other is an iterative algorithm that employs real-time field data, encompassing production and ESP operational data to dynamically recalibrate and update the digital twin representation of the well. By capturing the well’s dynamic state, the digital twin enables the optimizer, an optimization workflow that suggests optimal ICV positions and ESP pump frequencies, aiming to maximize oil production while maintaining water-cut constraints within individual layers. To validate the approach, we rigorously tested various well scenarios. Our approach involved flow tests with different ICV positions. In addition, we conducted a comprehensive parametric analysis, considering temporal variations in water cuts and the productivity index (PI) of individual layers. Recognizing that factors such as pressure fluctuations, wellbore conditions, and reservoir dynamics significantly impact overall productivity and inflow characteristics, the estimator within the digital twin avatar of the well is automated to allow fine tuning of PI and/or water cut for each layer to recalibrate itself dynamically. The estimator adeptly captures transient changes in the well, and our results demonstrate that initial calibration efforts substantially enhance its accuracy over time. The optimizer, an extension of the digital twin model, is tested against operational constraints of oil and water production to recommend ICV positions with projected flow rates and water cuts for each layer. Our findings align closely with a physics-based simulator, validating the approach within a 10% error range.

This entire digital twin workflow helps provide a consistent and reliable well monitoring mechanism for the well along with reliable recommendations for production optimization decisions. The novelty in this approach is to provide accurate real-time flowrate estimates of complex multilateral/multilayer wells with intelligent completions. The digital twin workflow provides virtual sensing that helps estimate downhole well conditions with great reliability for production management and optimization.