bluuebunny/crossref_metadata_2025_split huggingface dataset:
Causal Inference for the Characterization of Microseismic Events Induced by Hydraulic Fracturing
Authors: Conde Oliver Rojas , Misra Siddharth , Liu Rui 
Published: October 2023

Abstract
This study proposes a workflow that employs causal inference techniques on microseismic data acquired during hydraulic fracturing operations on 2 horizonal wells in Marcellus Shale. The study quantifies the causal relationships between a new microseismic event and the prior "spatiotemporally proximal" microseismic events, while taking into account the confounders that influence both the causes and effects. In doing so, we explain the magnitude, location, and occurrence of a new microseismic event produced during hydraulic fracturing as a consequence of the prior "spatiotemporally proximal" microseismic events. The causal relations quantified in this study are beyond statistical correlation/association tests. The study provides new insights into the microseismic-source mechanisms, such as: 1) Magnitude of a new microseismic event does not depend on the number and the spatial and temporal concentrations of the spatiotemporally proximal, prior microseismic events; 2) Regions with high microseismic magnitude events produce a new microseismic event earlier in time; and 3) Microseismically active regions produce a new microseismic event much earlier in time. Selection of true confounders is crucial for obtaining accurate causal estimates. Failure to properly select confounders can result in significant overestimation or underestimation of the causal estimates, as high as +/- 100%. Certain treatment-outcome pair exhibit large differences between the causal estimates and correlation coefficients that confirm the independence of causation and correlation. A causal analysis with true confounders reveals the true causal relationship that cannot be quantified using correlation/association methods.
HPHT Perforation Testing With Cesium Formate and Oil-Based Kill-Pills for North Sea
Authors: Le Guen Y. , Kumar A. , Jackson P. , McGregor J. , Grove B. , Rogers G. 
Published: October 2023

Abstract
A fluid with loss-control capability (kill pill) is used as the completion fluid during perforating to seal the formation immediately after perforating to maintain well control. This saves the time required to displace completion brine and the cost of losing the expensive brine into the formation. However, perforating with the kill fluid in the wellbore presents a great challenge in minimizing formation damage (Chang, Kageson-Loe, et al. 2004).
For wells requiring to be perforated with a static overbalance pressure (SOB) and subsequently shut in with the kill fluid in place (to maintain well control while retrieving the gun and installing the completions system), a properly designed dynamic underbalance pressure (DUB) should be used to surge all perforation skin damage (caused by perforation debris and grain crushing) out of the perforation tunnel and let the wellbore pressure recover back to overbalance rapidly. This sequence allows building of a thin yet effective kill-pill filter cake against the clean perforation tunnel wall (Chang, Mathisen, et al. 2005). Too little DUB could leave the perforations with significant perforation skin remaining, strongly impairing the productivity. Too much DUB has the potential to cause perforation cavities to collapse, which will also strongly impair the productivity.
Further, some kill fluids, such as a heavy brine formulated with cesium formate (CsFo) are significantly more costly than other kill fluids, such as an oil-based mud (OBM). Therefore, optimizing the perforating design and choosing the proper kill fluid are crucial tasks to ensure good well productivity (Chang, Kageson-Loe, et al. 2004).
This paper discusses how one North Sea operator conducted large-scale perforation and formation damage qualification testing of two perforating kill-pill candidates for use in completing HPHT wells located in the Central North Sea. Test program design, results, and implications are discussed. To the authors’ knowledge, novel information presented includes 16-hour shut-in leak-off responses, flow initiation differential pressure, and return cleanup flow data for kill-pills placed within real perforations (perforations created using a real explosive shaped charge), quantitative and qualitative descriptions (with photos) of resulting CsFo and OBM kill-pill external filter cakes, dye flow visualizations of production flow path into perforations having kill-pill filter cakes, and quantification of single-shot total perforation skin decomposed to effective crushed zone permeability and thickness.
Predicting Rig Engine Utilization Using Machine Learning to Drive Fleet-Wide Emissions Reductions Initiatives
Authors: Poludasu S. , Groh A. , Snijder van Wissenkerke M. , Harrist J. 
Published: October 2023

Abstract
With the drilling industry's focus on sustainability and digitalization, there are several data-driven studies published in the recent years which aim to reduce the operational fuel consumption and greenhouse gas (GHG) emission footprint through use of efficient generator management and alternative fuel sources. The possible savings from these optimization solutions are often measured with respect to a baseline - a model representing expected performance for the observed load profile. Since rig power demand and crew behaviors can vary immensely, estimating this baseline accurately can be challenging, while still being critical for GHG reduction initiatives to measure progress. This paper presents a novel data-driven methodology using machine learning to provide a real-time representative model of generator management.
Two years of generator power output data at 1 Hz sampling frequency was collected over 110 rigs for this study. From this data, multiple time-dependent features were created to capture and learn from trends in rig power requirements. An ensemble of rig-specific models was built using an ordinal classification algorithm to predict the number of generators online at any particular instant, while remaining true to the core behaviors across the fleet. Additional logic was added during testing to ensure results were realistic and interpretable by field personnel. This real-time model was then deployed to run on this contractor's edge-computing platform across its fleet of rigs.
Emissions metrics using the proposed methodology were one of the crucial results that encouraged the rig crews to reduce the fuel consumed over the duration of this study. These metrics were integrated into proactive reporting sent to rigs to improve their generator management practices and to highlight rigs with outstanding practices. Results on the impact of these initiatives on performance will be discussed.
The authors are not currently aware of any large-scale, data-driven approaches to estimating generator usage on drilling rigs. This approach offers new ways to assist and make an impact on fleet-wide GHG reduction initiatives. Additionally, the unique ensemble framework of this methodology can be customized and adapted easily to many applications involving timeseries prediction over multiple rigs / operating units. This paper also demonstrates the value of data monitoring and data-driven workflows in increasing the operational efficiency.
Electrical Treatment to Revive Dead Gas Wells due to Water Blockage
Authors: Aljuhani G. , Almuaibid A. , Ayirala S. , Qasim A. , Yousef A. 
Published: October 2023

Abstract
The occurrence of water blockage is a major concern for gas wells, which severely impacts the productivity. This phenomenon is due to the prolonged contact of surrounding region around wellbore with water thereby increasing the water saturation relative to gas saturation. Consequently, the pore spaces are completely occupied with water, blocking the flow of gas and thus reducing the gas production. In this paper, we propose electrical treatment as a potential solution to reverse the unforeseen water blocking process and revive dead gas wells to produce desired gas.
Electrical treatment involves the placement of two electrodes in between two spaced wells or within the same well, one acting as source and the other as a sink. One of these electrodes acts as a cathode, while the other as an anode to cover a reservoir region of around 2-3 km. After current is applied from power supply to well head, the charge will propagate through metallic casing along the well until pay zone delivering electric current to the reservoir. The electrical induced effects in the reservoir may vary according to the variation of the current density and voltage applied.
The tight and small pore throats will be enlarged by the application of electrical current. This results in an increase of pore throat radius due to motion of water molecules, cations and anions thereby releasing some of the water from blocked pore throats. Thus, permeability and subsequently relative permeability to water is increased. The local energy pulses will also cause partial electrolysis forming gas droplets besides enhancing the coalescence of released water droplets to form larger water ganglia. These larger water ganglia will sequentially grow to form a continuous film of water phase to minimize surface energy and ease the movement of water. The electrical treatment operation can take up to 30 hours with a long-lasting effect from 6 months up to 2-3 years.
The electrical treatment method described in this paper to revive dead gas wells is a sustainable and eco-friendly solution for easy practice in the field. This cost-effective approach can prolong the life of gas wells to increase the productivity.
Understanding Subsurface Uncertainty for Carbon Storage in Saline Aquifers: PVT, SCAL, and Grid-Size Sensitivity
Authors: Likanapaisal P. , Lun L. , Krishnamurthy P. , Kohli K. 
Published: October 2023

Abstract
Subsurface uncertainty has a great deal of impact on the development of oil and gas reservoirs, as demonstrated through decades of industry experience. Understanding uncertainty to facilitate robust business decisions across potential scenarios is the cornerstone of successful field development. Although carbon storage is also subject to subsurface uncertainty, the phenomena that impact storage efficiency may not be the same as those influencing oil and gas production. The objectives of this study are to utilize reservoir simulation to
Investigate how rock and fluid properties affect CO2 plume size, migration, and trapping mechanisms during- and post-injection, Perform grid size sensitivity to define resolution requirements, and Quantify the impacts of coarse simulation grid and the requirements of monitoring resolution.
We present how reservoir conditions (i.e., temperature, pressure, and salinity) affect fluid properties and carbon storage performance. Reservoir temperature and pressure are considered both independently and together along geothermal gradients. A similar investigation also provides the sensitivity result based on varying SCAL (i.e., relative permeability and capillary pressure) parameters. For the grid size sensitivity, the findings demonstrate that an accurate plume size requires a fine vertical grid resolution, while the areal grid resolution impacts the dissolution rate. We make gridding scheme recommendations for reliable predictions based on these findings. We also analyze the result to quantify the error due to coarser grid size and the requirements for appropriate monitoring resolutions.
The results from the sensitivity study can help categorize storage site potential. The grid size study provides crucial information to develop reservoir simulation best practices in evaluating carbon storage candidates. Another aspect of a carbon storage operation is the monitoring plan to ensure the containment of the injected CO2. Since the geometry of the plume continues to evolve post-injection, the simulated predictions can guide the selection of monitoring technology with appropriate resolution necessary to capture the CO2 plume at various timeframes.
Multizone Open Hole Gravel Pack Completion with Selective Production Capability in ACG Field – Azerbaijan
Authors: Susilo Yoliandri , Dharmadhikari Khsitij , Taha Sherif , Ismayilova Ayisha , Guliyev Ilkin , Jafarli Zaur , Kerimov Natig , Kerimova Ravana , Mammadov Elvin , Veliyev Samir , Wallace Alex , Whaley Kevin , Foster Mike 
Published: October 2023

Abstract
Azeri-Chirag-Gunashli (ACG) is a giant field located in the Azerbaijan sector of the Caspian Sea. The major reservoir zones are multi layers sandstone formations and weakly consolidated where Open Hole Gravel Pack (OHGP) completions have become the standard design for production wells. To date more than 170 high rate OHGPs have been completed that are producing comingled from multi-layered sandstone formation.
As the field matures, problems such as premature water and gas breakthrough are becoming increasingly common requiring the completion system to be inherently flexible to address such issues. The Multi Zone OHGP concept design has been developed to manage this increasing reservoir management complexity. Zonal isolation and selective production capability are achieved by installing combination of multiple Screen PBR and/or open hole packer in combination with seals unit and mechanical sliding sleeve in the inner string at the intermediate completion, and Inflow Control Valve (ICV) at the upper completion.
To date, two Multizone OHGP wells have been completed successfully. The Screen PBR system has proved to provide effective zonal isolation or baffling. This system allows flexibility to deal with unexpected reservoir surprise (wet/gas zone) that requires zonal isolation on day-1 without major changes in completion design, thus reducing rig time & operational cost.
This paper discusses design, execution, and result of the first two Multizone OHGP completions installed in the ACG Field. Installing multiple Screen PBR to provide baffling against crossflow is a novel concept. This technique does not compromise gravel pack quality or sand control integrity. The success seen with this technique makes a compelling case to further develop the concept on a larger scale in ACG and maximize field recovery.
Duplicate Detection of Technical Documents in Petroleum Context Based on Near-Duplicate Image Search
Authors: Maze Elie , Dirhoussi Amine , Vernaz Yann , Farahbakhsh Reza 
Published: October 2023

Abstract
Identifying technical document duplications is a crucial task to optimize storage resources and improve document management and retrieval processes. This helps to ensure that the most up-to-date version of documents is going to be available, which is important in all industrial domains including the petroleum sector to avoid errors and inconsistencies. In addition, deduplication is a key step to consider in data pre-processing for training large machine learning models. This paper tackles the task of duplicate detection in technical reports by proposing a hybrid solution based on open-source libraries on computer vision. The proposed solution has been evaluated on a large set of data and provided promising results which can help us to optimize our storage. We observed the solution is flexible enough to adapt to different scenarios and different types of reports. Having such a solution will potentially lead to reducing the carbon footprint in IT infrastructures.
Automatic Integration of While Drilling Cutting Descriptions with Formation Evaluation Models
Authors: Alakeely A. A. , Torlov V. S. , Qubaisi K. A. , Kanfar M. F. 
Published: October 2023

Abstract
The procedure of analyzing and integrating text information from cutting descriptions collected during drilling process with formation evaluation models is a tedious task that is demanding in terms of time and human labor. The work describes a process to automate the task and use it to identify hydrocarbon zones in exploration wells.
As the drill bit penetrates the formations in a well, the rock fragments are usually described across the associated depth of interest in a text format. These descriptions are valuable source of information that are usually compared to petrophysical models that uses wireline logs as input. In this work, textual cutting descriptions are utilized in identifying potential hydrocarbon zones using natural language processing and deep learning algorithms. The methodology uses natural language processing techniques to generate data set. The data set is used to train a deep learning algorithm to identify hydrocarbon zone from cutting descriptions automatically.
The system has been used to identify hydrocarbon zones in a group of exploration wells. The trained model has been applied to blind test wells. The methodology reached hydrocarbon detection accuracy of 98% in challenging areas. For example, it allows hydrocarbon detection in low resistivity pay reservoirs where Archie's based saturation calculations present a water wet zone. The result was confirmed by formation tester and testing results suggesting the correctness of the technique. Moreover, the automation reduces the turnover time of such analysis and reduces human error.
The enclosed process helps in integrating textual information into the petrophysical model to reduce uncertainty, human labor, and aid in identifying hydrocarbon zones. In addition, the method will reduce the need for unnecessary testing, resulting in cost saving in rig time operation.
Evaluation of Fluid Containment and Perforation Erosion in Multistage Fracture Treatment
Authors: Sakaida Shohei , Hamanaka Yasuyuki , Zhu Ding , Hill A. D. , Kerr Erich , Estrada Erick , Scofield Reid , Johnson Andrew 
Published: October 2023

Abstract
Fracture monitoring and diagnosis by fiber optic sensing technology provides invaluable information about stimulation efficiency. This technology becomes more critical for multistage fracturing over long horizontal wells. The combined analysis of distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) measurements has been used to map the fluid distribution during hydraulic fracturing, and fracture volume at the cluster level and at the stage level can be estimated from the analysis. In this paper, based on the interpretation of models for DTS and DAS, we extend the application to fluid containment and stage isolation evaluation.
In plug and perf hydraulic fracturing, when a stage is finished pumping, the stage is isolated by a plug, and the next stage is perforated then fractured. If the plug is not functioning correctly, fracture fluid enters the previous stage, causing an uneven fluid distribution, and possibly more severe perforation erosion. Both DAS and DTS monitoring can record this phenomenon when it happens. Interpretation models are built to quantify the fluid distribution with the effect of plug leakage included. From DTS measurements, cooling below the stage being completed is counted as the fluid from the current stage. Given this information, the reservoir thermal model can estimate the volume of leakage. From the DAS measurement, we first interpret the volume distribution based on frequency band energy level to compare with the DTS interpretation, we then use the volume to estimate perforation erosion. Perforation erosion measurements from a downhole camera are used to confirm the estimation. With this approach, we understand the fluid distribution, plug performance, and the importance of stage isolation on perforation erosion during a multistage fracture treatment. The information can then be used to analyze and optimize completion and fracture treatment design.
Field examples are presented in the paper to support the discussion, findings, and conclusions. Based on the results of fluid distribution estimated, we observed that higher injection rate and larger fluid volumes may create more uniformly distributed fluid. When considering communication between stages, the DTS and downhole camera show that there is a correlation between fluid distribution and perforation eroded area. In the completion design, only limited parameters show strong impact on fluid distribution and perforation erosion.
Successful Application of Intelligent Completions in a Deepwater Gulf of Mexico Water Injector
Authors: Bolingbroke H. F. , Saripally I. , Webb K. , Gandhi N. , Askeroglu I. , Beecher R. 
Published: October 2023

Abstract
In complex reservoirs with challenging operating conditions, intelligent completions have proven to be instrumental in optimizing water injection and conducting reservoir surveillance. In this paper, we present a multi-disciplinary case study from deepwater Gulf of Mexico (GOM) highlighting the critical role played by an intelligent completion in a dual zone water injector.
The GOM field in this study is a Pliocene-aged, high-density turbidite sand consisting of multiple stacked reservoirs. It is a mature field with more than 15 years of production and water injection. The hybrid wells are tied back to the offshore platform via individual marine risers, so well-bay slot availability is limited. Thus, a dual zone injector with downhole choking was planned to maximize wellbore/slot utility. The intelligent completion provided the ability to inject selectively at desired rates into each zone for waterflood optimization.
This case study demonstrates an integrated framework of planning, drilling, completing, and operating a state-of-the-art intelligent well in an offshore environment, including:
Wellbore planning: A dual-zone injector was chosen to make the best use of existing infrastructure and slot availability. Un-swept and pressure-depleted reservoir targets were identified through seismic and pressure data. Numerical models were built to aid with downhole choke design and to forecast the response seen in offset producers. Drilling and Completions: Factors such as wellbore availability, directional constraints, and collision avoidance were reviewed. Wellbore component selection included an intelligent completion with downhole choking and a triple downhole pressure/temperature gauge. Metallurgy specifications, jewelry positioning, perforation strategy, and frac design were considered. Operations: The ability to isolate either of the two completed zones allowed for high-confidence surveillance and waterflood optimization. Additionally, the intelligent completion greatly assisted with acidizing and adding waterflood tracers, as each zone could be treated or isolated individually.