bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-Time Bacteria Monitoring Applications in Oil & Gas Operations can Help to Prevent and Mitigate Corrosion
Authors: Steffensen Aurelija , Miller Morten , Elleskov Susan , Vilain Eric 
Published: September 2022

Abstract
Controlling bacteria in Oil & Gas operations is essential for flow assurance and avoiding unhealthy environments developing in the systems.
The resistance of bacteria grows stronger as they manage to survive environments, In gaming terms, the bacteria is "leveling up" every time it faces and overcomes adversities, thus getting more resilient. Once bacteria are present in medias of Oil & Gas, they will already be bacteria with high survival skills and with very complex development and growth path. Knowing the total content of bacteria can provide confident decision-making on aspects such as HSE, system integrity, biocide selection, and dose frequency.
As anaerobe bacteria are very harsh on metal structures, they can cause corrosion and damage and shortened lifetime on flow structures and pipelines. Ideally, bacteria are monitored closely with short testing times to ensure rapid decision-making and initiation of mitigating actions.
However, the traditional methods for detecting and enumerating bacteria in Oil & Gas operations are cumbersome and time-consuming, with analysis times of 24 hours to 28 days, making them inefficient in an operational environment that requires rapid decision-making to stay in control. Tools that address these limitations are thus in high demand.
In this study, we will describe how a field portable technology for fluorometric determination of a hydrolase activity can be used to identify and monitor bacterial contamination in an offshore operation in near real-time.The study demonstrates how the use of the enzyme method allowed the platform chemical engineer to quickly identify a bacterial contamination issue, related to a failed biocide injection pump and allowed the platform manager to rapidly initiate relevant mitigation of the problem.
Validation and Analysis of the Physics-Based Scaling Curve Method for Ultimate Recovery Prediction in Hy-Draulically Fractured Shale Gas Wells
Authors: Arias Ortiz Daniela , Bounceur Nabila , Patzek Tadeusz W. 
Published: September 2022

Abstract
Patzek et al. (2013, 2014) proposed the generalized physics-based scaling curve method as an alternative approach to the empirical decline curve analysis that predicted shale gas well production. Independently, (Patzek, 2019; Patzek et al., 2019) also introduced the Generalized Extreme Value statistics to evaluate cohorts of shale wells and their survival rates. In this work, we built a conceptual reservoir model of a typical, hydraulically fractured well in the northeast Pennsylvania Marcellus shale using a commercial reservoir simulator (CMG-GEM) to validate the generalized master curve numerically. We use the simulated gas production as the input data to estimate the generalized reservoir scaling curve, and we compare it to the master curve presented in Saputra et al. (2022). Our results reveal that the physical scaling method captures the physics behind gas production from mudrock plays. Our reservoir simulations agree with the master curve. We conclude that the simple method is an excellent alternative to the current industrial forecasting methods as it is computationally cost-effective, more flexible, and requires fewer input data. Also, the numerical reservoir simulations confirm the behavior of the generalized scaling curve with the variation of selected input factors. We complemented this study by conducting a global sensitivity analysis (GSA) to systematically examine the impacts of hydraulic fracture half-length and spacing, unstimulated shale permeability and gas adsorption on the variations of two master curve scaling parameters, the gas mass in the stimulated reservoir volume (MSRV), and the characteristic pressure interference time (τ). GSA using a reservoir simulator is prohibitive. Therefore, we implement and validate a Gaussian process emulator that represents probabilistically the scaling parameters estimated from the reservoir simulation output. We calibrate the emulator with a small set of experiments sampled with a space-filling design. The conducted study provides new insights into the relationship between the production scaling variables MSRV and τ and the reservoir parameters. The results reveal the high importance and nonlinear effects of the hydraulic fracture height, half-length, maximum gas volume adsorbed, and matrix porosity in varying the scaling variable MSRV. Also, the unstimulated matrix permeability and the hydraulic fracture spacing contribute significantly to nonlinear variations of the scaling variable, τ. Finally, gas adsorption has a small effect on the cumulative gas produced but significantly affects the scaling factor MSRV. Thus, gas adsorption becomes essential when estimating the ultimate recovery factor in the Marcellus shale wells.
Automating Log Interpretation for Optimal Well Placement and Increased Drilling Efficiency
Authors: Jonsbråten Fredrik , Iversen Marianne , Røsvik Jensen Kåre , Vik Constable Monica , Haktorson Hilde , Holbrough David , Wessling Stefan 
Published: September 2022

Abstract
The Troll oil field has been one of the largest oil producers on the Norwegian continental shelf for the last 25 years and is now moving into late life. The remaining oil column is thin, and the fluid contacts vary due to production effects. To extend field lifetime and secure the last reserves, enhanced well placement and increased drilling efficiency is needed to reduce cost and increase recovery in the long multilateral horizontal wells. Due to thin oil column and low reserves number, every meter correctly placed in the reservoir counts. To investigate these challenges a technology development project was initiated between Equinor and Baker Hughes to develop automatic interpretation of the oil-water contact (OWC) based on inversions, and automatic steering advice for taking faster geosteering (RNS) and downlink decisions placing the wellbores at the optimal distance to the OWC.
Automating log interpretation is a complex task, but solvable given a known environment. As an engineering problem it must be split up into multiple smaller tasks that can be independently solved and when combined, solve the greater task. Logging while drilling (LWD) deep azimuthal resistivity data is run through inversion processing which provides a resistivity profile from which the OWC position is identified. The inversion input model constraints are set based on field/area specific data and is run automatically as drilling progresses.
To assess the quality and validity of these results, several flag curves are computed and used. This automatic quality control of the OWC points enables the creation of a forward projection of this boundary. A steering advice is calculated, giving a recommendation on how to achieve the desired stand-off and inclination above the OWC as efficiently as possible.
All the output from the automatic interpretation is published to a central datastore and is immediately available for the geoscientists to optimize operational decisions.
Close co-operation between the operator and the vendor during the development and testing of the service has proven beneficial for identifying areas for further improvements. The service has been tested for well placement in actual producers.
Several loops have been made in the development between the different tests and the learning curve has been steep in both companies. Based on the experiences and results from the actual wells, the project has moved into a new phase for further optimization of the steering advice and linking the automated steering advice to an Automatic Drilling Control (ADC) system to deliver a more automated closed loop service.
An Engineered Microparticles-Based Slurry Pumped in Over 10,000 Stages Provided Notable Operational and Production Improvements in Challenging Formations
Authors: Radwan Amr , Ramanathan Raja , Ivanishin Igor , Ibrahim Ahmed Farid 
Published: September 2022

Abstract
In some shale plays, insufficient formation breakdown and presence of near-wellbore tortuosity make it challenging to reach the designed pumping rate and lead to premature screen-outs. Screen-outs during a fracturing operation are a tremendous burden for operators as they diminish the well's total production and add cost to do a wellbore cleanout. In some cases, these issues could cause suboptimal perforation cluster efficiency and production loss. There is a critical need for an easy-to-implement solution that can help operators in achieving their desired fracture designs. This paper presents field case studies of a new microparticles-based slurry (MPS) technology that proves ease of operations and an improvement in production across four different US shale basins.
Non-hazardous water-based slurry contains engineered glass microparticles with a median size of 550–625 mesh. It was implemented in the Rockies, Powder River, Permian, and SCOOP/STACK with over 10,000 stages stimulated so far. The slurry was usually deployed as an additive to the pad or as a pill before pumping the proppant-laden slurries. It is compatible with commonly used fracturing fluids. The MPS technology helps in scouring the perforations and lessening fracture entry restrictions. This results in better fracture initiation and lowers the screen-out potential. The technology also widens fracture openings, restricts fracture complexity, reduces near-wellbore tortuosity, and increases reservoir connectivity. The slurry can be used as a far-field diverter pill as well.
Field studies in multiple challenging formations involving alternating stages between the microparticle slurry and the standard control showed a 12–25% reduction in pump time due to significant pressure relief. In another pad, the MPS reduced the screen-outs by over 6 folds. Production data showed up to 19% uplift within a 15-month period against control wells. The production improvement analysis is a subject of further study. Oil and water tracer tests confirmed the production improvement in stages that had the microparticle slurry. Overall, the success rate of the technology has been unprecedented and has been gaining significant ground over the past year.
Realizing a treatment design is a critical step in maximizing the rate of return on a well. This new chemical slurry offers operators a simple, cost-effective, and field proven solution to alleviate operational issues and potentially be more aggressive in completion designs. The diverse case studies in this paper prove the efficacy of this innovative technology in solving the major day-to-day fracturing challenges faced by completion engineers.
An Investigation into Non-Reservoir Components that Undermine Reservoir Responses in Transient-Test Data
Authors: Rahman N M Anisur , Sarac Sukru 
Published: September 2022

Abstract
Transient-test data can be significantly affected by subtle non-reservoir noise generated by natural or operational factors. The impact of such noise on the pressure-transient analysis depends on the signal-to-noise ratio achieved during transient tests. Often, a misleading interpretation of the reservoir characteristics can result. This study will quantify such effects on the interpretation results by evaluating the signal-to-noise ratio as in wireline testing operations, deep transient testing, drill-stem testing and production testing.
Effects of non-reservoir factors can be difficult to identify, and often may lead to misrepresentations or misinterpretations. Analytical and numerical reservoir simulations will be used to illustrate quantitative criteria of defining the acceptable operating conditions and preferable techniques for pressure-transient-tests, depending on the reservoir characteristics. Convoluted effects of noise, drift, resolution, periodic tides have been quantitatively evaluated to demonstrate the situations when the reservoir signal is too weak to achieve meaningful characterization. Different pressure-transient techniques will be evaluated with a focus on the signal-to-noise ratio.
Certain disruptive behaviors of equipment and nature tend to distort the measurements performed during such tests. Depending on the amount of disruption caused in the measurements during the tests, there are situations when the test objective may not be achieved at all. Failure to create dominant reservoir responses can result from an insufficient signal-to-noise ratio with the rate of production and pressure drawdown. It is a function of formation and fluid properties and/or mechanical environment. A minimum rate of production is needed for creating a necessary magnitude of signal-to-noise ratio to interpret correctly the reservoir response. The paper will help determine the minimum rate of production and the duration of flow needed to obtain the presence of deep heterogeneities or boundaries with a reasonable level of certainty. If a test is run with a rate lower than the critical value, for example, the data will be biased by other hardware or natural factors that are unrelated to the reservoir signals. Illustrative examples will also be presented to show how misleading characteristics of the reservoir and the well can be deduced without sufficient signal-to-noise ratios.
This study will quantify the non-reservoir factors by evaluating the corresponding signal-to-noise ratios. As a result, a practical guide will be created for selecting a proper testing method from a quantitative point of view.
Using AI Cuttings Load Classification to Assess Hole Cleaning and Wellbore Stability
Authors: Holt Calvin , Ruel Francois 
Published: September 2022

Abstract
This paper discusses a field test that placed computer vision cameras on rigs' shale shakers to measure drilled cuttings and cavings. The images from the camera were processed using proprietary computer vision software and sent to the drilling team, where it was used to assess drilling performance, hole cleaning, and wellbore stability. This Proof of Concept (PoC) test was conducted over five months on two drilling operations in the Marcellus shale, using three different camera types placed in two shaker locations. Over 200,000ft of video data from 11 wells were collected from the West Virginia / Pennsylvania regions. This paper describes the research and development of this technology's hardware (camera) and software (computer vision).
To the author's knowledge, this is the industry's first use of computer vision to measure drill cuttings autonomously and continuously at the rig site. Computer vision applied at the shaker will increase overall ROP, greatly improve the speed and accuracy of wellbore instability detection, reduce the exposure in a hazardous zone and improve the detection of pending downhole problems.
Adapting Petroleum Reservoir Engineering Principles to Carbon Capture & Sequestration (CCS) and Hydrogen Underground Storage (HUS) Projects: Opportunities and Challenges
Authors: Mishra Srikanta , Datta-Gupta Akhil 
Published: September 2022

Abstract
Carbon Capture and Sequestration (CCS), which combines capture of CO2 from large stationary sources with geological storage, has emerged as an attractive option for emissions reduction. Hydrogen underground storage (HUS) is viewed as an effective strategy for storing large volumes of surplus electrical energy from renewable sources. The objective of this paper is to discuss the opportunities and challenges for adapting petroleum reservoir engineering techniques for the subsurface aspects of CCS and HUS projects based on a critical review of field projects and conceptual studies. Areas of focus include: (a) storage resource estimation, injectivity analysis from field data, dynamic reservoir modeling, and coupled flow and geomechanics for CCS, and (b) well deliverability, dynamics of fluid withdrawal and reactive transdport of hydrogen in-situ for HUS projects. Specifically, our goal is to discuss how traditional workflows for oil and gas applications have been (or could be) modified for CCS projects in deep saline formations and HUS projects in salt caverns or aquifers. We also identify specific areas where reservoir engineering practitioners can add value in CCS and HUS related reservoir analysis and modeling.
Comparative Analysis of Data-Driven, Physics-Based and Hybrid Reservoir Modeling Approaches in Waterflooding
Authors: Mogollon Jose Luis , Tillero Edwin , Calad Carlos , Lake Larry 
Published: September 2022

Abstract
Hydrocarbon production optimization is essential in pursuing the best scenarios for economic outcomes. But because of complex and multi-dimensional nature of production processes, thousands of scenarios are possible. Extensive data collection may allow uncovering patterns still unidentified. With on-site computing power increasing, cloud availability, and artificial intelligence evolution, mathematical optimization methods are becoming powerful and accessible. Data type-tailored models are implemented for history matching and prediction of operational efficiency of the asset. This paper presents a comprehensive analysis and comparison of three data type-tailored reservoir modeling methods and their optimization process for waterflooding field cases.
The mathematical techniques used were Data-Driven Capacitance Resistance Model (CRM), Numerical Simulators (Data-Physics) coupled to Smart Algorithms Optimizers, and Hybrid Model (Machine Learning Physics-Based). They were compared to 1-identify the benefits of mathematical optimization techniques, 2-illustrate the methods developed to sort out time and computing capacity restrictions, and 3-validate the techniques by comparing the forecast with actual results.
The six study cases of different reservoir types in Argentina, Venezuela, and the USA, had different types data availability. Four had no static model. In two cases, field results were available to confirm the accuracy of the forecasted injection and production. The forecasted increase in Net Present Value (NPV) and cumulative oil production (Np) ranged to 30%, and optimized water injection rates decreased by 50%.
Traditional modeling techniques yielding unreliable result in one field with hundreds of producing layers and unknown lateral and vertical continuity were solved using a machine learning technique. In some cases, they pointed toward non-intuitive infill drilling sequence and injection water redistribution. Also, they pointed to options that reduce economic risk. The methods yielded many better economic scenarios and increased the flexibility of operationalizing plans. In one field requiring excessive computing power, using time horizons reduction and successive year-by-year optimization yielded 4 times the NPV of the base case. This approach solves objections related to long computing time and system instability. With the three mathematical techniques, the asset value could be continually maximized by a novel implementation of a heuristic decision-making approach that continuously challenge the current scenario. It makes a systematic formulation of conceivable new scenarios, competing through an objective function determining the probity of compared scenarios.
The optimization also resulted in an up to 50% decrease in water injection requirements and the same percentual CO2 emissions reduction.
The Impact of Petrophysical Heterogeneity on the Carbon Capture Utilizations and Storage Strategies
Authors: Alqahtani Fahd Mohamad , Althehibey Mohammed Talal , Ghasemi Mohammad 
Published: September 2022

Abstract
Carbon capture utilizations and storage strategies (CCUS) have recently attracted many interests to mitigate the global warming and reducing CO2 emission in the atmosphere. Injecting CO2 into the depleted hydrocarbon reservoirs and aquifers has been focused as one the main CCUS strategy. This paper presents a comprehensive study on the effects of reservoir heterogeneity that are defined as important physical criteria to determine the success of CCUS projects.
We develop an equation of state (EOS) fluid characterization with 34 components. Wide range of fluid composition from black oil to gas condensate system was generated to capture the degree of fluid heterogeneity in the reservoir. The results of various PVT tests such as CCE, swelling test with CO2 and MMP were compared for different heterogeneous fluid systems. The pseudoized EOS model was optimized to a minimum number of pseudo components that reproduce the physical properties of full-EOS model. The detailed EOS model was then employed by a full compositional reservoir simulation model to study the CO2 injection in a depleted oil reservoir with high degrees of heterogeneities. Various range of porosities, permeabilities, formation temperature and injection pressure scenarios were considered during sensitivity analysis of CO2 storage in depleted reservoir.
Prior to CO2 injection the reservoir depleted from initial reservoir pressure to its abandoned pressure which resulted in forming a gas cap zone with low residual oil saturation. Case scenarios with homogeneous fluid composition, and petrophysical heterogeneity at different reservoir conditions where considered for initialization of the reservoir sector model. Results show that the oil saturation at the abandoned pressure is strongly dependent to the initial condition which effect the CO2 storage efficiency at the later period. During CO2 injection period, the oil recovery profile and the CO2 storage are considerably affected by reservoir heterogeneity, reservoir conditions and different injection scenarios. High CO2 storage was achieved with high CO2 injection pressure with a certain degree of heterogeneity and at low reservoir temperatures.
This paper provides a better understating of CO2 storage mechanism under various range of petrophysical heterogeneity and different reservoir conditions.