bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Method for Accurate Measurement of Fluid Saturation in Shaly Sandstone During CO2 Sequestration
Authors: Gao Jidong , Yuan Bin , Zhang Wei , Zhang Hongbin
Published: February 2024

Accurate monitoring of fluid saturation is crucial for preventing fingering or leakage during CO2 sequestration. Due to the difficulty in testing the cementation factor of shaly sandstone, the evaluation of fluid saturation in such reservoirs by the Archie equation may be significantly biased. This study presents modifications to the Rhoades capillary bundle model and the Waxman-Smits parallel model for the clay conductivity phase and cementation exponent, based on the principle of the best conduction pathway. we use two different conductivity models and fluid transport coefficients in rock pores to modified the cementation coefficient. We compare different conductivity models and refine them by fitting them to on-site CO2 storage monitoring data. We analyze the impact of parameter variations on conductivity and saturation, and verify the accuracy of the equation. It is found that the relative error of the modified model is 10.76% compared with the conductivity of the dual-water model. Shaly sandstone reservoirs are characterized by clay-water expansion phenomena, with clay conductivity comprising the predominant fraction of total conductivity. The variation in water phase transmission and conductivity can be divided into two phases. In the first phase, conductivity experiences a rapid increase, while in the second phase, it rises gradually in a linear fashion. In shaly sandstone reservoirs, the relative error of the modified model is 5.44%. The enhanced accuracy in saturation calculations serves as a safeguard against measurement errors by on-site engineers.
Hydrogen Price Race in Australia
Authors: Escobar-Burnham Esther , Szemat-Vielma Waldemar
Published: February 2024

In December 2018, the Council of Australian Governments set a vision (Australia Hydrogen Strategy) for a clean, innovative, safe, and competitive hydrogen industry that benefits all Australians and will be a major global player by 2030. This study will summarize the current and forecast uses and volumes of hydrogen along with estimating the potential future range of hydrogen prices and prices’ key drivers across Australia seven territories.

The seven hubs are the basis to understand the range in hydrogen prices combined with the technologies available to produce hydrogen across Australia. These seven hubs are planned as a springboard to large scale production, although hydrogen projects can be outside these hubs as well. The impact of the drivers and price assessment across the green and blue hydrogen technologies were studied. Hydrogen price ranges were calculated and are based on the levelized cost of hydrogen (LCOH) for the different electrolysis technologies as well as, steam methane reforming, black coal and lignite gasification with carbon capture and sequestration.

The price is equivalent to a Free on Board (FOB) or at wellhead price. The model was built to replicate the Commonwealth Scientific and Industrial Research Organization (CSIRO) LCOH for the mentioned technologies, which was then used to model the price for green and blue hydrogen across the seven states and territories. Since the model is based on LCOH it does not incorporate additional costs such as transport or company overheads and market forces such as supply and demand. A comparison of the hydrogen price for various generation methods across the states and territories of Australia was generated and analyzed. The key observations from the results include: 1) there is neither a leading technology nor a leading territory for the low or high hydrogen price; 2) variations in energy commodity prices are directly linked to the hydrogen price and the main factor to consider with green hydrogen production; 3) decreases in costs suggest efficiencies in technology across the green and blue hydrogen industry are starting to be realized; 4) short-term volatility is expected as the industry develops but over the longer term the price should stabilize towards the lower end of the range.

With Japan is a strong partner aiming to reach hydrogen cost of A$3/kg., Australia must reduce the hydrogen production cost as much as possible to keep its competitive advantage amongst competitors. This paper will describe the hydrogen price calculation process, the variables, and considerations for each of the seven Australian territories to become the top producer and exporter of the region.
Machine Learning Application on Seismic Diffraction Detection and Preservation for High Resolution Imaging
Authors: Bashir Y. , Khan M. , Mahgoub M. , Ali S. H. , Imran Q. S. , Imran C. , Karaman A.
Published: February 2024

Capturing small-scale features in complex subsurface geology, such as Carbonate, through seismic imaging poses challenges due to the influence of heterogeneous properties of objects in the subsurface on propagated waves. The first step in machine learning (ML) involves supplying a sufficient amount of data to ensure the learning algorithm is updated and matured. In the absence of multiple shapes of diffraction data, the accuracy of your prediction for machine learning (ML) may be compromised. ML may also fail to detect the pattern of diffraction in the data. Following the learning process, our machine focuses on the critical task of target detection. This involves comparing the target with the given data and searching for a specific signature. In this study, data is provided to the system in the form of images and features. The learning algorithm can process the input to make predictions about the target. The concept behind machine learning is to minimize the discrepancy between your prediction and the target value as much as feasible. The preservation of diffraction amplitude in laterally varying velocity conditions is influenced by certain factors. The technique of ML destruction is employed to separate diffraction data, as traditional filtering methods tend to blend diffraction amplitudes in cases where there are one or multiple diffractions present.
Modeling of Low Salinity Polymer (LSP) Interactions in Carbonates from Geochemical and Surface Chemistry Perspectives
Authors: Hassan Anas M. , Adila Ahmed S. , Al-Shalabi Emad W. , Tellez Arellano Aaron G. , Kamal Muhammad S. , Patil Shirish
Published: February 2024

Low Salinity Polymer (LSP) injection is a hybrid synergistic enhanced oil recovery (EOR) technique that improves displacement and sweep efficiencies by combining the advantages of both low salinity and polymer flooding methods. Nevertheless, proper design of LSP flooding at field-scale requires a predictive mechanistic model that captures polymer-brine-rock (PBR) interactions. Therefore, this study investigates the impact of water chemistry on polymer behavior in porous media in order to gain a better understanding of the PBR-system. In particular, we examine the effect of salinity and hardness on polymer viscosity and adsorption in dolomite formations during LSP flooding employing our in-house coupled MRST-IPhreeqc simulator. Furthermore, to capture the geochemistry of the LSP process, the MRST-IPhreeqc simulator incorporates surface complexation reactions as well as aqueous, mineral dissolution and/or precipitation reactions.

The findings of this study suggest that the 5-times spiked salinity and hardness scenarios are more favorable than those of 10-times spiked salinity and hardness, which were supported by their respective polymer viscosity losses of 75% and 82% for salinity spiking, and 58% and 63% for hardness spiking. Also, the effects of 10-times spiked Ca2+, 10-times spiked Mg2+, and 2-times spiked SO42-on polymer viscosity were studied with estimated viscosity losses of 61%, 61%, and 46%, respectively. The latter signifies the importance of sulfate spiking for reducing polymer viscosity loss while avoiding exceeding sulfate limit for scale formation and reservoir souring. For the effect of salinity on polymer adsorption, it was observed that the increase in salinity from the base case scenario (623 ppm) to 5- and 10-times spiked salinity, results in an increase in the dynamic polymer adsorption from 53 μg/g-rock to 59 and 68 μg/g-rock, respectively. Additionally, comparing the 10-times spiked Mg2+, 10-times spiked Ca2+, and the 2-times spiked SO42- scenarios, the 10-times spiked Mg2+ case resulted in the maximum polymer adsorption (87 μg/g-rock). This is due to the surface complexation reactions of magnesium surface species at dolomite rock surface with polymer molecules forming Mg-polymer surface complexes. In contrast, the calcium and sulfate do not form surface complexes through reactions with the polymer. This indicated that the divalent cation's design might impact the viscosity of the LSP solution, and therefore, it is crucial to carefully consider it when optimizing the LSP process in carbonates. Thus, proper design of LSP flooding at field-scale requires a predictive mechanistic model that captures PBR interactions which is covered in this work.
Improving Well Accessibility and Productivity with Multi-Lateral Tool Technology
Authors: Mashhad H. S.
Published: February 2024

Multi-lateral wells are a widespread choice for reservoir engineers to maximize reservoir contact. However, these complex wells pose challenges for rig-less well interventions, especially during acid stimulation. Multi-lateral tool technology can be used to improve well accessibility and optimize well productivity. This paper presents a comparison of the performance of several multi-lateral tools in dual-lateral, tri-lateral and quad-lateral Oil Producer and Water Injector wells.

Variety of tools, including hydraulically activated tools, electrically rotated tools, as well as hybrid hydraulic and electrical tools. The latest technological advancement in the Multi-lateral Wellbore access industry which is the integration of an azimuth determination unit to the multi-lateral tool assembly is compared with older technology in terms of performance and efficiency. The execution challenges, lessons learned, comparison between multi-lateral tool types, including the average time taken to access each lateral and the success rates are analyzed in detail and presented.

Deployment results showed that the multi-lateral tool technology significantly improved well accessibility during acid stimulation. When combined with advanced fiber optics coiled tubing technology, the success ratio was observed to increase significantly. It was found that the electrically activated and electrically rotated multilateral tool equipped with azimuth determination was by far the most effective way to steer coiled tubing into wellbore laterals. The lessons learned from this comprehensive analysis could be utilized to optimize future applications of multi-lateral tool technology. For example, the data collected from the fiber optics coiled tubing could be used to develop new tools that are even more effective at accessing and stimulating multi-lateral wells.

Overall, Multi-Lateral tool technology is a promising solution for the challenges of rig-less well interventions in multi-lateral wells. The technology can improve well accessibility, optimize well productivity, and provide a safe and efficient alternative to bull-heading.
Study on the Influence of DFN on Hydraulic Fracture Propagation for Horizontal Wells in Unconventional Resource - A Case Study from China
Authors: Wang Hui. , Zhou Wei. , Wu Rangbin. , Shang Yamin. , Li Zheng. , Li Guohao. , Huangfu Xuerui. , Zhao Mengjie. , Liu Xiaolong. , Wu Yutong. , Lian Liming. , Huang Xingning.
Published: February 2024

Fractured reservoirs are distributed with natural fractures of various shapes and sizes, which are the main reservoirs. Due to the uneven distribution of natural fractures, the current main method of fracture reforming is to communicate the natural fractures through hydraulic fractures and to establish a flow channel between the fractures and the wellbore. Clarifying the expansion law of hydraulic fractures in fractured reservoirs is conducive to enhancing the effect of reservoir modification. In this paper, a numerical model of hydraulic fracture extension in a reservoir based on a discontinuous discrete fracture model is established. Firstly, a reservoir fluid-solid coupling stress model is established, and then the discrete fracture model is used to construct the hydraulic fracture, which allows the hydraulic fracture to expand along the initially delineated mesh, and the minimum strain energy density criterion is used to determine the expansion path. According to the different distribution patterns of the seam holes, three reservoir types with fracture characteristics are established in this paper. The simulation results of reservoirs with different natural fracture characteristics show that natural fractures perturb the local stress field and deflect the extension path of hydraulic fractures. The more developed the natural fractures are, the more pronounced the perturbation effect is. According to the different relative positions of hydraulic fractures and natural fractures, they can be divided into frontal repulsion and lateral attraction, which are not conducive to the communication between hydraulic fractures and natural; increasing the net pressure inside the hydraulic fractures can enhance the dominant role of the hydraulic fractures when they intersect with natural fractures, and reduce the degree of deflection, which is conducive to the breakthrough of the natural fractures’ repulsion, and communicate with natural fractures in the direction of the dominant stress. The pressure at the injection point when the hydraulic fracture penetrates the natural fracture is mainly controlled by the flow energy loss in the hydraulic fracture and the leakage rate of the natural fracture. The optimization of the fracturing fluid performance can reduce the penetration pressure and enhance the extension range of the hydraulic fracture. The results of this paper provide references for the fracturing modification evaluation of fractured reservoirs.
Case Study of Drag Reducing Agent Application in Produced Water Reuse: Produced Water Throughput Improvement, Total Cost Reduction, ESG Enhancement
Authors: Moringo Nicole , Malekahmadi Fatee , Li Yifan , Price Brian , Muhammad Moin , Young Moosaa
Published: February 2024

Objective/Scope

In response to environmental, social, and governance (ESG) regulations, the industry has shifted to using produced water for hydraulic fracturing. However, the present infrastructure limits the volume of produced water that can be transferred. As infrastructure is not easily replaced, a water-based drag reducing agent (DRA) can chemically alter pipe friction. Formulating a DRA with shear stability and chemical compatibility in produced water remains a significant obstacle and requires thoughtful and specific chemistry.

Methods, Procedures, Process

A comprehensive water analysis of produced water is carried out, which includes examining the ion composition, total dissolved solids (TDS), total suspended solids (TSS), total organic carbon (TOC), and other chemical residues. Flocculation tests and flow loop experiments are performed to assess the chosen DRA's compatibility and drag reduction performance in the produced water. Finally, the text presents a successful field trial that involves treating and transferring over 2 million barrels of produced water in the Permian Basin, USA.

Results, Observations, Conclusions

The field trial demonstrated that the application of DRA significantly improved the water transfer rate for produced water. This improvement allowed for the use of produced water with the existing infrastructure and eliminated the need for fresh water on the job, which was not achieved previously. The use of DRA also led to a reduction in fuel consumption and equipment wear and tear. In addition, the field trial demonstrated that using DRA on the frac pad had extended benefits, as DRAs applied upstream remained fully soluble throughout the job. The properties of the DRA were not altered during water transfer, enabling the use of the same DRA on the pad. The trial results indicated that DRA could reduce the time required to reach the design rate and decrease wireline run time, ultimately resulting in time, chemical, and cost savings.

Novel/Additive Information

The specific chemistry resulting in DRA compatibility with produced water can reduce the friction reducer needed for the hydraulic fracture design, which makes the addition of a DRA cost-neutral while yielding numerous benefits aligning with the increasing ESG metrics in the oil and gas industry.
Role of Fluid Injection in Induced Seismicity
Authors: Conde Oliver Rojas , Misra Siddharth
Published: February 2024

Correlations between the fluid injection via water disposal (WD) wells and hydraulically fractured (HF) wells into subsurface earth and the subsequent increase in seismic activity is well documented. Our research presents a method to go beyond statistical correlations and quantify the causal relationship between subsurface fluid injection and induced seismic events, accounting for the confounding factors. The dataset employed for this analysis covers a 7-year period within the state of Oklahoma. The dataset comprises details such as the locations and key operational metrics of the water disposal and hydraulically fractured wells. Additionally, the dataset contains the location and magnitude of earthquakes, as well as the location and length of major fault lines. For this study, earthquakes (which can be induced seismicity) have a magnitude higher than 2. Our dataset contained 22,368 earthquakes ranging from magnitude 2 to 5.8 with a mean of 2.47 and mode of 2.2. Utilizing double machine learning (DML), we estimate the average treatment effect (ATE) to precisely quantify the causality. ATE results were derived from diverse spatiotemporal sample combinations, pinpointing the areal extent and temporal duration of heightened causality. This enabled us to explore the relationships between seismic activity and WD/HF wells in terms of the areal and temporal extents of the effects of the fluid injection into the subsurface through WD or HF wells. The ATE results reveal that 2 active water-disposal wells over 56 days cause 1 earthquake within a 4,400 sq. km area. Hydraulically fractured wells were found to have a more localized causal impact, such that 3 hydraulically fractured wells over 106 days lead to 1 earthquake within a 200 sq. km area. No detectable causal effect of fluid injection on earthquakes magnitude was identified.
Physics-Informed Machine Learning Application to Complex Compositional Model in a Giant Field
Authors: Bascialla Guido , Rat Coriolan , Sheth Soham , Dias Daniel , Heidari Mohammad Reza
Published: February 2024

Compositional reservoir simulation is a time bound activity demanding complex physics. We review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties, such as critical temperature and saturation pressure. A machine learning approach to predict critical temperatures during simulation based on the Heidemann-Khalil method is implemented, resulting in more accurate results with lower computational cost, outperforming the standard method and improving performance on a giant field model with compositional gradient and miscible gas injection.

The fluid column grades from black oil to gas condensate; accurate phase behavior and miscibility modelling involves a significant number of components. This makes simulation performance one of the biggest challenges. Critical temperature of the mixture is commonly used to determine phase state (phase labeling), a crucial process of reservoir simulation. Mislabeling can result in incorrect physics and convergence issues, particularly in cells with gas-oil displacement. Simulators generally use the Li correlation to calculate the pseudo-critical temperature from the weighted average of component critical temperatures, leading to inaccuracies. The Heidemann-Khalil method is computationally costly, proportional to the cube of the number of components, prohibiting its use for complex compositional simulation models. We use machine learning to efficiently incorporate it into simulation.

By using the machine learning approach, neural networks are trained based on a combination of feeds to reproduce the Heidemann-Khalil method with great precision. The accuracy of critical temperature and other fluid property determinations is thus improved, with machine learning ensuring a very low computational cost. Convergence is also improved. With the traditional Li method, especially at the beginning of gas injection, we faced numerical difficulties, and the runtime was slowed down. By implementing the machine learning based method, the convergence is smooth through the entire gas injection cycle, leading to a reduction of total iteration counts. We experience an overall four times speed-up of the simulation model, which greatly enhances the usage of this model in simulation studies.

The use of machine learning methods to replace physics in the simulator is an evolving area. By showing a field example of a successful application that improves both accuracy and performance, we contribute to fostering research into new possibilities where physics-informed models will enhance simulation studies.
Unleashing the Potential of Rate Transient Analysis: Transforming the Exploitation of Mature and Tight Fields, 40-Years Mature Field Case Study
Authors: Ali Amr Gharieb , Gabry Mohamed A. , Algarhy Ahmed , Ibrahim Ahmed Farid
Published: February 2024

This paper aims to investigate the challenges faced in the development of mature and tight fields, primarily resulting from reservoir depletion, high operational costs and reserves volumetric calculations uncertainty. The objective is to address the power of old, cheap data integration with the rate transient analysis technique. Additionally, the absence of sophisticated reservoir modeling data further complicates the situation due to the wells’ development many years ago. However, the paper intends to explore the potential of diverse sets of production data over an extended period, available for interpretation and integration with production operations data, to revolutionize the exploitation of mature and tight fields.

The paper investigates the effectiveness of Rate Transient Analysis (RTA) in mature fields. This study introduces an innovative workflow that leverages existing data sources, such as Net Liquid above Pump (NLAP) and SRP SAM panel data, which was previously overlooked, to demonstrate the remarkable advancements made possible through RTA, particularly in the late-life development of mature fields and leverages the possibility to revive the old field again and convincing the management to re-invest in such fields. This research presents compelling outcomes achieved by applying the proposed workflow to old tight reservoirs.

The results reveal a successful correlation between initially calculated static volumetric reserves and the lately dynamically calculated reserves, affirming the accuracy of the new RTA/Production data integration workflow in evaluating reservoir potential. Moreover, the study identifies previously unrecognized drilling locations and optimal fluid withdrawal spots, facilitating precise exploitation of these tight reservoirs. Additionally, the research uncovers the optimal spacing necessary to maximize permeability and drainage area in these formations, unlocking their full potential for enhanced recovery.

The findings presented in this study significantly contribute to a deeper understanding and optimization of mature field operations by harnessing the power of RTA in conjunction with specialized operations such as hydraulic fracturing, operators can design optimal stimulation plans to further enhance reservoir performance. Additionally, integrating RTA results with simulation studies helps reduce uncertainty in history matching and forecasting, enabling more accurate production plans.
Enhancing Safety in Geological Carbon Sequestration: Supervised Machine Learning for Early Detection and Mitigation of CO2 Leakage in Injection Wells
Authors: Harati Saeed , Gomari Sina Rezaei , Rahman Mohammad Azizur , Hassan Rashid , Hassan Ibrahim , Sleiti Ahmad K. , Hamilton Matthew
Published: February 2024

The efficient and safe operation of CO2 injection wells during geological sequestration is crucial for successful carbon capture and storage (CCS) projects. This study explores the application of machine learning in creating a data-driven model for simultaneous prediction of the location and size of potential leak incidents along an active CO2 injection well based on wellhead and bottom-hole pressure and temperature data. Five different well-established machine learning algorithms were selected for predictive model development, including Support Vector Regression (SVR), K-Nearest Neighbor Regression (KNNR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and Artificial Neural Network (ANN). A series of numerical simulations were performed to create a dataset based on a CO2 injection well model in a southern North Sea saline aquifer reservoir, accounting for various leak scenarios with different locations and sizes. The dataset includes three input features of wellhead pressure, bottom-hole pressure, and bottom-hole temperature, paired with two output variables of leak location and leak size. The research findings demonstrate that all models perform well in effectively pinpointing leak locations, but they face difficulties when it comes to detecting small leaks, particularly those with a CO2 leakage rate below 0.01 kg/s. The results obtained indicated that, with regard to model performance, the SVR and KNNR models tended to outperform the others during the testing phase. More precisely, the SVR model demonstrated exceptional performance in the context of leak localization, particularly when dealing with smaller datasets. Conversely, KNNR consistently showcased superior performance in the detection of leak size, regardless of the dataset size. The outcomes of this research can provide valuable insights into the behavior of leaky CO2 injection wells during geological sequestration and highlight the efficacy of supervised machine learning in detecting and predicting leakage in CO2 injection wells.
Relationships Between Rock Acoustic Dispersion and Petrophysical Properties for Enhanced Formation Evaluation
Authors: Ali S. , Ma S. , Al-Ofi S.
Published: February 2024

Understanding the dissipation of acoustic energy in a fluid-saturated rock formation can divulge some important clues about their petrophysical properties. Rock properties such as bulk density, porosity, permeability, fluid type and viscosity affect the acoustic propagation velocity. The objective of the present work is to study the effects of the afore mentioned petrophysical properties on the acoustic dispersion phenomenon at a variety of practical application frequency scales.

Experimentation was performed on sandstone rocks to measure petrophysical properties such as bulk density, porosity, permeability and acoustic velocities of compressional waves sweeping frequency, ranging from downhole logging (~20 KHz) to laboratory ultrasonic measurements (1 MHz). Sandstone samples, with different range of porosity and permeability were selected in order to study their effect on acoustic dispersion. Dispersion analysis for each rock type was performed and related to the petrophysical properties.

Acoustic velocity measurement method at variable frequencies is described in this study. Laboratory measurements of p-wave velocity at variable frequencies ranging from 60 KHz to 1 MHz were performed on sandstone rocks of different porosity and permeability. Biot-Gassmann and squirt-flow models were applied to describe the p-wave velocity dispersion behavior. The dry measurements showed insignificant change in p-wave velocity with respect to frequency which reaffirms the claims of earlier studies of frequency independence of acoustic velocity at dry condition. The squirt-flow model predicted a transition from low to high-frequency regime at or above logging frequencies. These theoretical models can be used to derive acoustic velocities at any required frequency ranging from seismic to logging and ultrasonic frequencies. Squirt flow model can be used to derive rock permeability given the characteristic squirt-flow length is known and model parameter Z derived from laboratory measurements. Samples with higher porosity exhibit more dispersion compared to lower porosity samples can give a qualitative measure of the porosity. Further research and development in this area may lead to improved methods for characterizing sandstone reservoirs and optimizing hydrocarbon exploration and production.

The novelty of the present workflow lies in utilizing and extracting additional potential of acoustic data to estimate petrophysical properties from multi frequency modern acoustic logs.
Dissolvable Ball as a Temporary Fluid Loss Control Device for Multilateral Wells
Authors: Mallawany I. El , Simmons Y. , Mohamed A. , Osman A.
Published: February 2024

Commercially available fluid loss control devices (FLCDs) require intervention or the application of pressure to reopen the well for flowback and production. In some multilateral applications, well architecture may not allow re-entry to the laterals for mechanical intervention and/or the lateral junction (for TAML ≤ 4) might not be pressure isolated to allow pressuring down the lateral. This paper discusses the use of dissolvable balls to act as a temporary FLCD and thus, eliminate the need for an intervention operation or pressuring up the junction to open it. Additionally, an overview of lab tests and field trials performed will be discussed.

Different dissolvable ball materials were tested. The test was designed to mimic the target well conditions using the target well's completion fluid, pressure and temperature. The ball that was best suited for the application was then selected for the field trial. A dedicated baffle was run as part of the lower completion inside the lateral, a few casing joints above the shallowest frac sleeve. After completion of all stimulation stages, the dissolvable ball was dropped from surface and pumped to the seat on the dedicated baffle mitigating fluid losses. No losses were observed while pulling the frac string and the multilateral (MLT) deflector out of hole, nor during upper completion installation. The dissolvable ball also did not affect the production flow during flowback operations. This was accomplished without intervention or the application of pressure through the TAML level 4 junction.

Dissolvable balls can be successfully and safely used as a FLCD in lateral branches of MLT wells. This was proven through lab testing and field trial. The main advantage of this method over commercially available FLCDs is eliminating the need for intervention or wellbore pressurization. Furthermore, this method simplifies the operation, reduces risks and provides time and cost savings. Meticulous planning and testing are key for achieving success for such applications having strict time constraints to safely proceed with the remaining operations in the completions program. In the discussed case study, the ball provided up to 17 days of safe operations without losses as the upper completions was set and tubing hanger landed. Furthermore, during flow back operations of the lateral branch, which started 18 days after setting the upper completion (35 days after ball launching), no traces of the ball were observed.

This paper describes the novel use of dissolvable balls as FLCD where traditional FLCDs are not adequate. Additionally, dissolvable balls offer simplicity as well as time and cost savings over other traditional fluid loss control devices. (El-Mallawany et al., 2022)
Machine Learning accelerated Phase Flash Calculation for CO2–Brine System considering Capillarity Effect
Authors: Aslam Billal , Tariq Zeeshan , Yan Bicheng
Published: February 2024

The geological sequestration of carbon dioxide (CO2) has been proposed as a critical solution to mitigate climate change. Accurate prediction of CO2 solubility in brine is necessary for a better estimate of CO2 storage capacity in subsurface reservoirs. Specific Equation of State (EOS) models coupled in compositional reservoir simulators are usually used to predict the fate of dissolved CO2 over time. However, this approach can be computationally expensive, particularly if a more detailed physics description is used, such as capillarity, which is important in describing phase behavior in the cap-rock layers. To address this issue, a Machine Learning (ML) based approach is used to generate a proxy from high-fidelity physics simulations describing CO2-brine phase behavior. First, Peng-Robinson EOS, coupled with Duan & Sun model are used to generate approximately 5000 samples of CO2 solubility calculation dataset for ML model training. Several ML models such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) are then trained and compared to predict phase split calculation result and CO2 solubility. Potential ML model is then optimized by hyper-parameter tuning to increase prediction accuracy. Sensitivity analysis from the trained model was performed to evaluate the behavior of model prediction. The trained DNN model shows promising potential to accelerate Pressure-Volume-Temperature (PVT) and solubility calculations, with a test case showing approximately a 210 times speedup with an Average Absolute Percentage Error (AAPE) of less than 0.117% compared to the EOS calculation.
Representative Elementary Volume as Key Parameters to Improve Grid Stability on Multi-Point Statistic Based Reservoir Models
Authors: Budiman Ose , Azhar Allende G. A. , Setyadi Abdul Latif
Published: February 2024

Reservoir modelling plays a significant role in the investigation of reservoir heterogeneity related to the distribution of reservoir zones and lateral and vertical variations in geological and petrophysical properties of the reservoirs. Data and information derived from the core, well logs, and seismic could be used for facies and petrophysical modelling of these reservoirs. How to organize the data, how to apply the limited spatial offered by the data, and how to develop a comprehensive reservoir model by using only a few points of data are all different problems when using this data as the input for reservoir modelling. Using the Multi Point Statistics Method (MPS) to build the facies as similarly as possible to the geological feature is one of the best ways for modelling the facies. Therefore, the level of uncertainty could be reduced. However, the training image used to build the facies model must be accurate and precise. It is essential to gain as much geological insight as possible from the well logs, seismic, and core data before translated into the training image as a conceptual model. The data input could be optimized using modern tools and techniques, leading to excellent outcomes. This study discussed and dive into the various ways in which the MPS technique can be improved, such as in terms of grid stability, error reduction, well logs’ correlation to the training image, and minimize the uncertainty. The best and most effective techniques to portray a reservoir with good quality grid without scarifying the detail were determined by using the Representative Elementary Volume (REV) as the key parameter to determine layering and zoning. The result showed less discrepancy between the model and the well logs’ data, more stable grid, and will expect to be impacting the simulation progress by lowering the running time. Proper utilization of training images is also required for the geological feature to accurately record observations. It is shown that using REV for zoning and layering will improve the efficiency and accuracy of the 3D facies model.
Seismic Attributes Contribution for CCUS Monitoring Projects
Authors: Amrouche M.
Published: February 2024

Seismic attributes are mathematical representations of various physical properties of the subsurface reservoir in seismic data, such as seismic amplitude, phase, frequency, and velocities. They are widely used in modern seismic interpretation workflows and can bring additional value at different stages of Carbon Capture, Utilization, and Storage (CCUS) projects, as they provide an additional insight that cannot usually be achieved in the original seismic data. Seismic attributes can be used to identify potential CO2 storage layers, monitor the behavior of the CO2 plume during injection and migration and model or extract the exiting volumes from the data.

In this study we assess the applicability of some selected seismic attributes routinely used in seismic interpretation, to enhance CCUS interpretative workflows such as stored carbon monitoring, migration path and volume extraction. We used the Sleipner Carbon Capture Storage (CCS) project 4D seismic dataset (Norway) to demonstrate the ability of structural and stratigraphic attributes to enhance mapping and monitoring of the injected CO2 plume through the years. Results from our experiments shows that structural seismic attributes can help to enhance the resolution of the original seismic data and obtain higher resolution imaging of the injected plume, by comparing changes in seismic amplitudes at the different stages of the injection. Stratigraphic attributes helped to improve the plume signature through the different stages of injection and extract the final shape of the injected carbon from seismic data as discrete geobodies. Interpretative Workflows using seismic attributes discussed in our paper can be replicated to other CCUS projects, and help to understand the injected carbon behavior, optimize injection strategies and ensure a safer and more effective storage process.
First LWD NMR Acquisition in 8.5-inch Extended Reach Well
Authors: Soma Ahmed M , Manuaba Ida Bagus Gede Hermawan , Hursan Gabor G , Steene Marie Van
Published: February 2024

Logging-While-Drilling (LWD) nuclear magnetic resonance (NMR) data was acquired in an extended reach well targeting a carbonate formation to obtain rock quality and permeability data. The challenge was to minimize lateral motion that could affect the NMR data quality.

Pre-job modeling was performed to evaluate the bottom-hole-assembly (BHA) stability and optimize the drilling parameters. A novel system using downhole drilling mechanics measurements while drilling was set up to evaluate the effect of drilling mechanics on the NMR sensor while drilling. Several intervals were relogged to compare the drill pass data and the reaming data, to confirm the absence of lateral motion affecting the data.

The LWD NMR data was acquired while drilling over just under 4000 ft. The well experienced a build of inclination to optimize reservoir contact. The downhole drilling mechanics flag showed a medium risk of lateral motion, also highlighting the absence of stick and slip while drilling. The LWD NMR drill pass and ream pass data compared well, confirming the absence of significant lateral motion effect on the NMR sensor while drilling. The relog passes experienced more stick and slip than the drill passes, due to the low rotations per minute (RPM) used during ream passes acquisition. The NMR highlighted a good relationship between porosity and pore size distribution in both reservoir sections.

For the first time in the region, LWD NMR data is acquired in the 8.5-inch section in an extended reach well with good quality NMR data.
Using Pressure Transient Analysis as an Effective Surveillance Technique to Locate the Remaining Oil in a Sandstone Heavy Oil Reservoir Under Water-Flood
Authors: Almahrouqi A. , Govinda P. , McCaslin J.
Published: February 2024

This paper aims to present a case study of the results of several falloff surveys done in relatively heavy oil field. It focuses on interpreting pressure transient analysis (PTA) in a pattern-developed, water-flooded field. The objective of the paper is to demonstrate that by utilizing a pressure falloff survey, it is feasible to detect the change of mobility (K Kr/µ) away from the water injector, resulting from the transition to higher oil saturation.

In subject field, the oil exhibits a viscosity that is 90 times higher than that of water. In principle, this significant contrast can be identified through the pressure response, indicated by a shift in mobility away from the well. The geology around the well, seismic interpretation and well history are critical in understanding the boundary responses in the PTA.

A comparison between 3 falloff surveys will be discussed: A newly drilled injector, a one-year injector and a five-years injector.

New injector: A pressure falloff with a simple response can be seen where the early-time region is dominated by skin. Followed by radial flow and no boundary response. Interestingly, the mobility obtained from the radial flow period is very low suggesting that the formation is predominantly saturated with oil. One-year injector: The radial flow region is small, however, unlike the first injector, the mobility in the initial radial flow is higher compared to first injector however, it reduces after 7 meters inside the reservoir (Ri) which then stabilize at 1/5th of the original mobility. Five-years injector: Similar to case 2, the radial flow region stabilizes at a mobility similar to the expected from petrophysical analysis. However, after a distance of 45m, the mobility reduces to 1/6th of the original. This indicates an increase in oil saturation beyond the 45m mark, away from the injector.

After considering all potential boundaries that could result in this response, the most reasonable and likely scenario is a change in fluid saturation from water to oil away from the injector. This is supported by the good correlation found between cumulative water injected and the distance at which the mobility reduces in PTA. This finding suggests that falloff can be utilized to track the advancement of the waterfront, particularly when a significant mobility contrast exists between the fluids. This also means that an average oil saturation away from the injector could be approximated enabling the detection of un-swept areas in the field.

Given the significant viscosity contrast between water and oil, the Falloff survey, when integrated with other data sources, can be employed as a cost-effective and straightforward surveillance tool to identify the waterflooded area surrounding the injector and track waterfront progress.
A Streamline Based Lagrangain Method to Investigate Two-Phase Flow in Hydrocarbon Recovery
Authors: Ahammad M. J. , Rahman M. Azizur , Alam J. M.
Published: February 2024

The study of miscible flow is an important topic in the field of petroleum science and engineering. The miscible displacement of hydrocarbon-like oil by a solvent such as carbon dioxide helps to enhance the recovery of the hydrocarbon. The use of CO2 helps to reduce the viscosity of resident fluids like oil, In addition, the use of CO2 would reduce its accumulation in the atmosphere to help a green environment. The production of hydrocarbons in the secondary or tertiary phase follows the mostly miscible flow. The Computational Fluid Dynamics (CFD) simulations can help with better understanding enchanch oil recovery in the two-phase flow system. The traditional simulations of such flow suffer numerical artifacts due to the appropriate methods. We present a generalized CFD model for studying the transient methodology for momentum transfer. We investigate an upscaling approach to resolve the small-scale features of miscible flow in a porous medium. A streamline-based Lagrangian model is developed to study the displacement of oil by CO2 with sufficient accuracy and near-optimal computational cost.
How the Memory Multidetector Pulse Neutron Logging Helped to Achieve a High-Quality Results While Reducing the Costs – Case Studies in Malaysia
Authors: Loo S. , Willick J. , Pemper R. , Mekic N. , Khalid K. Binti , Johare D. , Goh A. , Huong C.
Published: February 2024

Managing reservoir performance and monitoring is an ever-increasing challenge in offshore brown field environments where cost optimization becomes more critical. The limitation of wireline accessibility on the wellsite tends to evaluate an opportunity to run the memory Multidetector Pulsed Neutron (MDPN) logging technology allowing a cost-effective and conveyance-unrestrained option for reservoir evaluation.

The typical wireline conveyed service of a pulsed neutron technology with a five-detector array has proven to be reliable in Field A, offshore Malaysia. It was successfully run on across gas and oil intervals behind multi-tubing completions and complex downhole conditions. The utilization of slickline conveyance on memory allows the reduction on well-site manpower, spacing and equipment. Slickline operation requires the addition of a battery pack with an integrated tool controller, memory module and safety sub to the standard logging suite. Extensive laboratory testing, simulation and well tests were conducted to assure reliability of the memory MDPN system to deliver wireline-equivalent results.

Memory MDPN with slickline offers the ability to capture critical Carbon/Oxygen and advanced gas measurements for oil and gas saturation analysis as well as capture the fluid movements using a time-lapse reservoir evaluation. Interpretation of memory MDPN data delivered information on fluid contact movement within the complex multi-stacked siliciclastic reservoirs. Results were validated by correlation with production history of the subject wells and corroborated with past pulsed neutron results from nearby wells. The MDPN technology has proven beneficial for complex wells with its advantages of a large detector array, integrated response characterization modeling, and wireline-equivalent data. As an added advantage, the reduced operational footprint saved the operation ~54% in cost compared to a standard wireline operation since wireline skid unit and crane were not required. Additionally, operational simplification, easier mobilization and streamlined operations lead to the reduction of personnel risk exposure, energy, emissions, and waste.

Effective reservoir evaluation in brown fields remains an important activity for operators while maximizing the potential production from a reservoir. With the introduction of a memory pulsed neutron logging system, operators can now perform cased hole evaluation in a more efficient and cost-effective manner, achieving similar data quality to conventional wireline logging even in complex logging environments.
One MrSET Test Example in the Complex Underground Structure Area
Authors: FuHao JIANG , YuDong NI , WuHai YIN , Di WU , NaiJian WANG , XingGang LIU , ZhiHui YAN , WenJian LI , XiChang HOU , MingYi HAN , Jie SHENG
Published: February 2024

Multi-rate seismic exploration technique (MrSET), which is based on the pre-stack migration imaging (PreMI) theory, has achieved appreciated result in the field test in a basin in West China, where the surface terrain is simple and the underground structures are not very complex relatively. In order to further verify the applicability of the technique, while make the method and the technique more perfect, a swath 3D field test is carried out in another basin in West China, where underground structures are complex relatively. Identical shooting parameters are applied in the test to compare the imaging effects of the data acquired at the same receiver points of the same line using two different node layouts (the conventional regular layout and the MrSET layout designed based on the PreMI theory). The test results show that the imaging of data acquired using the MrSET layout is better than that of the data acquired using the conventional regular layout method.