bluuebunny/crossref_metadata_2025_split huggingface dataset:
PVT Data Making the Difference: Delineating the Size of Gas Caps in Stacked Reservoirs
Authors: in ‘t Panhuis Peter , Van Jaarsveld Jort , Cornelisse Peter , Ramaswami Shyamalan , Janson David , Rien Descamps 
Published: January 2023

Abstract
A proper characterization of fluid contacts and their uncertainties are an essential input to the development of gas and oil reservoirs. However, during exploration and early appraisal there is usually little information available that can reliably anchor the uncertainty ranges. Depending on the expected fluid type there may be little infrastructure in place to evacuate the reservoir fluids. Particularly the presence of a large gas cap can have a big impact on the economics of a potential development, depending on the evacuation options for the gas. This paper aims to show how PVT data may be used to delineate the size of the gas caps in stacked reservoirs.
Evaluation of Different Numerical Techniques for Accurate Modelling of Tracer Flow in Porous Media
Authors: Yaralidarani Muhammad , Aghabozorgi Shokoufeh , Farzaneh Seyed Amir , Sohrabi Mehran 
Published: January 2023

Abstract
Inter-well tracer tests have numerous applications in determining the volumetric sweep, levels of heterogeneity, and delineation of flow barriers such as the faults in subsurface in reservoirs. Tracer tests are also performed in the laboratory to determine core samples’ heterogeneity. Simulation of physical dispersion requires refined models, which inevitably increase the numerical dispersion in the obtained results. In this paper, we quantify the numerical dispersion associated with various techniques available for the simulation of tracer flow. Numerical dispersion in the simulations can be quantified by comparing the simulation results with the experimental data. For this purpose, we first reviewed the fundamentals of tracer flow and introduced the Convection-dispersion equation as a basic model to describe tracer flow in porous media. Then we constructed a refined model to simulate a series of tracer flood scenarios. A detailed sensitivity analysis was carried out in a systematic manner to specify the individual impact of physical and numerical dispersion. Finally, we modelled tracer experiments performed on Indiana Limestone (IL) carbonate rocks to examine the accuracy of Fick's and Darcy's equations, and the results are presented. The impact of both numerical (non-physical) and physical dispersion was examined during the core scale simulations. It was concluded that the numerical TVD algorithm (embedded within the commercial ECLIPSE software) can appropriately model the tracer flow in porous media with minimal numerical errors during simulations. It was also shown that physical dispersion significantly affects tracer test results and that it must be considered when simulating tracer flows by defining an appropriate Peclet number. Finally, the results showed that solving the conventional convection-dispersion equation along with the numerical TVD algorithm can perfectly match the experimental data of several tracer flood tests performed on outcrop Indiana limestone core samples.
Integrated History Matching Method for Horizontal Wells Using Advanced Pressure Transient Analysis
Authors: Ma Ruicheng , Wang Shu , Zhao Yixuan , Li Yong , Hu Dandan , Zhao Zeqi , Hao Siying , Chen Yihang 
Published: January 2023

Abstract
Horizontal wells have been applied in Middle East universally. However, with complex geological conditions including thief zone, aquifer and heavy oil, there are diverse production/injection performances for horizontal wells. Therefore, history matching is a tough job for reservoirs with horizontal wells. Pressure transient analysis (PTA) data is abundant and relatively economically obtained. With PTA technique, reservoir properties, streamline distribution and effective horizontal interval can be obtained easily. Therefore, history matching results could be identical with actual reservoir performance.
This paper proposes an optimized history matching method for horizontal wells assisted by advanced pressure transient analysis. Considering heterogeneity of layer-cake reservoir, a newly corrected method of log-log plot of PTA analysis is built to make a better interpretation under the effect of high permeability zone (HPZ). Then pressure transient analysis results will be validated with production/injection performance and other surveillance data such as resistivity logging. Finally, based on interpreted results, history matching is conducted. The advanced PTA model yields favorable interpreted results in accordance with production/injection performance and other surveillance data. Integrated history matching yields better results compared with typical workflow and modification of dynamic model. For several tough problems such as layer-cake reservoir with bottom aquifer, profile tuning is better than modification of grid property around wellbore.
Application of Historical Pressures for Mapping Reservoir Heterogeneity in Brown Fields
Authors: Kayode Babatope Omotayo , Stephen Karl D. 
Published: January 2023

Abstract

Summary
After a reservoir's structural framework has been established using seismic and well tops data, a conceptual heterogeneity pattern is used to control the spatial distribution of core and log data. Traditionally, seismic acoustic impedance or depositional facies map are the conceptual heterogeneity patterns used. Sometimes, facies map may be derived from limited wells, and seismic data is expensive to acquire. In such cases, a purely statistical approach like sequential gaussian simulation is used and the resulting heterogeneity pattern may have large uncertainty, being driven by probability rather than actual data. The goal of this study is to derive a new conceptual heterogeneity pattern using inexpensive and readily available historical pressure data.
It is known that pressure cluster differentiation can be caused by faults or fractures, we investigated in this study whether a non-faulted and non-fractured reservoir can exhibit pressure cluster differentiation. Numerical experiments are performed using hypothetical models to study the impact that stepwise and continuous permeability variations, the permeability ratio, duration of shut-in, and flowrate all have on the historical pressure trends of wells located within regions of similar and different permeability magnitudes. The historical pressures for all wells are plotted on the same axis to assess cluster differentiation. Closed polygons are drawn around the wells within each differentiated cluster.
The simulation results indicate that pressure cluster differentiation can occur in non-faulted and non-fractured reservoirs and typically implies heterogeneity. Therefore, in a reservoir where pressure cluster differentiation is observed, mapping the spatial locations of wells within each pressure cluster would result in a reservoir heterogeneity map which is shown to be comparable to seismic acoustic impedance map in an example dataset. A key contribution of this study is the development of an alternative conceptual heterogeneity pattern, using inexpensive and readily available data, that can be used as a trend map for guiding spatial property distribution during geo-modeling. An application example is used to further strengthen the conclusions derived from numerical experiments.
An Innovative AI Physics Hybrid Technology to Overcome the Time-Intensive History Matching Challenges, A Case Study: Minagish Oil Field
Authors: Qubian Ali , Zekraoui Mohammed Ahmad , Mohajeri Sina , Mortezazadeh Emad , Eslahi Reza , Bakhtiari Maryam , Al Dabbous Abrar , Al Sagheer Asma , Alizadeh Ali , Zeinali Mostafa 
Published: January 2023

Abstract
Reservoir simulation is the main factor in decisions made by oil companies in reservoir management. However, the simulation of huge and complex oil reservoirs through a time-saving and high-accuracy method is the primary concern in reservoir simulation. In this study, a novel AI-Physics hybrid model was proposed for combining with the traditional reservoir simulation to overcome the time-intensive history matching challenges. A combination of classical numerical simulation and deep learning neural network was applied to train the hybrid model with historical data. As a result, a model was obtained with predictive capabilities to forecast the field's behavior. Then, we combined AI-Physics history training with blind test prediction calculation of remaining oil maps. Finally, forecast scenario definitions based on the remaining oil map were created by the AI-Physic model. The proposed novel simulation method can reduce the history matching and scenario assessment time by 90 to 95%. According to its capabilities, three improved forecast scenarios were created based on a predefined scenario. These improved scenarios can produce a significant million standard barrels more oil than the original development scenario within three years. This technology eliminates limitations for multiple scenario assessments. In our AI hybrid model, the power of dynamic reservoir simulation is combined with a modern machine learning approach to "Evergreen" forecasts in reservoir assets. Consequently, the simulation resulted in a sub-optimal shortcut between model updates and inconsistencies in production forecasting. Moreover, applying deep learning methods to focus on the critical reservoir properties intelligently leads to tremendous time-saving in the static model update life cycle. In fact, with this novel simulation that we implemented, the new production data could be incorporated within minutes to regenerate more reliable and up-to-date forecasts. This simulation generates ‘up-to-date’ remaining hydrocarbon maps interactively, so the operator can continuously optimize the infill drilling locations between Field Development Plan (FDP) cycles.
Trial and Assessment of a New Downhole Sand Production Measurement in Oil and Gas wells
Authors: Eltaher Yahia , Nasser Mamdouh , Tarihoran Anton , Zaouali Zouhir 
Published: January 2023

Abstract
Sand production is one of the major issues in unconsolidated sandstone gas and oil reservoirs. The amount of the produced sand in gas wells might vary from a few grams to a few kilograms per hour, yet it can lead to the erosion of downhole and surface equipment and to partial or complete filling of the perforations, the screens or gravel pack completions downhole, hence localizing and quantifying the amount of sand downhole is a necessity for sand production management and sand shut-off, and to update accurately the existing geomechanical models to save huge costs involved from the CT clean-out operations and the repairs caused by erosion.
A newly developed downhole sand production measurement was successfully tested. The tool incorporates an innovative design that enables enhanced sensitivity to minute sand entries within the wellbore with minimal interference from the logging environment. This novel measurement was trial tested in different well completions. The carefully designed logging programs involved combining production logging tools – either conventional or array logging tools – along with the downhole sand detection tool for comprehensive log data interpretation. In this paper we illustrate with examples the results of the trial test and pinpoint the advantages as well as limitations of this new technology.
Log results in the vertical gas well were generally superior to those from other wells. Where the tool illustrated high sensitivity to locating small grains of sand production across the perforated intervals, as well as providing representative qualitative assessment of downhole sand production volume. Yet, most importantly it helped obtain the optimum drawdown for the free sand rate in this well. The tool was deployed also in one deviated oil well and two horizontal gas wells. These wells are completed with smart completions, sand screens and frac ports. The results helped reveal the efficiency of the screens and the integrity issues of frac ports and determine future plan to patch the frac ports to shut-off sand production. It is generally observed that log data quality and interpretation were challenged by the downhole logging environment and the well condition during log data acquisition.
The new downhole sand detection technology shows added value to allocate and qualitatively quantify sand production, check the integrity of sand screens and gravel packs, and several other applications. Candidate selection, job design and real-time log monitoring are crucial for the optimum benefit of the logging survey.
Carbon/Oxygen Spectral Data Processing, its Affiliation to Scintillation Detector Selectivity & their Impact on Reservoir Saturation Monitoring, Lessons Learnt and Recommended Workflow
Authors: Eltaher Yahia , Ma Shouxiang 
Published: January 2023

Abstract
For decades, it has been affirmed that pulsed neutron (PN) spectral Carbon/Oxygen (C/O) logging is the industry's most robust salinity-independent means for reservoir saturation monitoring (RSM); yet C/O logging still comes with considerable uncertainty that has to be identified and handled with ultimate care. In this paper we investigate two main aspects of such uncertainties and showcase some recommendations to enhance the accuracy of the measurement for improved reservoir saturation monitoring.
Two fundamental factors affecting C/O measurement are the type of gamma ray (GR) scintillation detector crystals used and the method for C/O spectral data processing. Currently, there are mostly six types of crystals used as GR detectors in commercial PN logging tools for routine operations. Each detector type has its advantages and limitations. With respect to data processing, the most commonly adopted method is the Windows method, due to its simplicity and statistical robustness. Whereas the Yields method is much more complicated to develop and prone to statistical variation, though it tends to provide more accurate results. Similarly, each of these two methods has its own set of advantages and disadvantages.
A comprehensive study involved different logging instruments and datasets acquired under various logging environments showed that both the physical properties of the detector, as well as the characteristics of the data processing method, have to be fully considered for optimum results. The Windows method, for instance, can be adequate for detectors of statistical nature. Unlike the Yields method, which requires an optimized set of detector and tool specifications. Where for certain GR detectors, significant differences in C/O data and consequently the calculated fluid saturation were observed when processed by using the Windows and the Yields methods. C/O data processing method selection is commonly fit for purpose; yet with the continuous advancement in GR detection technology, standardization is recommended for accurate and precise log measurement.
Accuracy and precision are keys to C/O logging and consequently successful reservoir surveillance and oil field management. Accordingly, a new standard RSM workflow is recommended where all available elements are properly tailored, to enhance the quality of the answer product.
Reservoir Modeling and Optimization Based on Deep Learning with Application to Enhanced Geothermal Systems
Authors: Yan Bicheng , Xu Zhen , Gudala Manojkumar , Tariq Zeeshan , Finkbeiner Thomas 
Published: January 2023

Abstract
With the energy demand arising globally, geothermal recovery by Enhanced Geothermal Systems (EGS) becomes a promising option to bring a sustainable energy supply and mitigate CO2 emission. However, reservoir management of EGS primarily relies on reservoir simulation, which is quite expensive due to the reservoir heterogeneity, the interaction of matrix and fractures, and the intrinsic multi-physics coupled nature. Therefore, an efficient optimization framework is critical for the management of EGS.
We develop a general reservoir management framework with multiple optimization options. A robust forward surrogate model fl is developed based on a convolutional neural network, and it successfully learns the nonlinear relationship between input reservoir model parameters (e.g., fracture permeability field) and interested state variables (e.g., temperature field and produced fluid temperature). fl is trained using simulation data from EGS coupled thermal-hydro simulation model by sampling reservoir model parameters. As fl is accurate, efficient and fully differentiable, EGS thermal efficiency can be optimized following two schemes: (1) training a control network fc to map reservoir geological parameters to reservoir decision parameters by coupling it withfl ; (2) directly optimizing the reservoir decision parameters based on coupling the existing optimizers such as Adam withfl.
The forward model fl performs accurate and stable predictions of evolving temperature fields (relative error1.27±0.89%) in EGS and the time series of produced fluid temperature (relative error0.26±0.46%), and its speedup to the counterpart high-fidelity simulator is 4564 times. When optimizing withfc, we achieve thermal recovery with a reasonable accuracy but significantly low CPU time during inference, 0.11 seconds/optimization. When optimizing with Adam optimizer, we achieve the objective perfectly with relatively high CPU time, 4.58 seconds/optimization. This is because the former optimization scheme requires a training stage of fc but its inference is non-iterative, while the latter scheme requires an iterative inference but no training stage. We also investigate the option to use fc inference as an initial guess for Adam optimization, which decreases Adam's CPU time, but with excellent achievement in the objective function. This is the highest recommended option among the three evaluated. Efficiency, scalability and accuracy observed in our reservoir management framework makes it highly applicable to near real-time reservoir management in EGS as well as other similar system management processes.
Identifying New Behind Casing Opportunities Using Machine Learning
Authors: Fadhil Imran M , Shah Jamari M , Sansudin Salmi , Abdollahzadeh Asaad , Husiyandi Husni , Azizul Nur Aimi Azimah , Hasnan Fairuz Hidayah , Thai Yuan Jiun 
Published: January 2023

Abstract
This paper discusses the adoption of Machine Learning (ML) approach to identify new Behind Casing Opportunities (BCO) in two brown fields (B and S) offshore East Malaysia. A multi-stage field-based ML models were developed based on selected wells and consequently used to predict reservoir characteristics in completed wells. The predicted results indicated new upside BCO for add perforation candidate.
Raw and interpreted data from B and S fields were analyzed and processed for model training and evaluation. For the case of identifying new opportunity, a specific model development strategy and train dataset selection was employed. The trained ML models evaluated to select the optimal models to predict lithologies, porosity, permeability and water saturations which are then been compared against the actual interpretation. Eventually, the identified upside potentials are validated by Subject Matter Experts (SME) before being proposed as add perforation candidate.
It was observed that the models’ performances vary between the two fields due to unique geological complexity as well as the varying quality of raw and interpreted data from each field. Field B which is more geologically complex performs less compared to Field S.
In conclusion, this study provides and insight on the advantages and limitations of machine learning to identify new upside BCO in completed wells. The novelty in this work is in the specific model development strategy to identify new upside BCO potentials.
This work may be beneficial and essential especially in enhancing resource monetization in brown fields which face challenges in terms of high idle well percentage, low recovery, and declining production.
Data Integration as a Key of Reservoir Characterization and Optimize the Well Placement. Abu Dhabi Offshore Case Study
Authors: Elabsy Eslam Mohamed , Soliman Ahmed Mohamed , Deng Lichuan , Aouacheri Amel , Alabed Sundos Ibrahim , Honda Kazumasa , Alsabeai Jawaher Adel 
Published: January 2023

Abstract
Carbonates heterogeneity is a continuous challenge for subsurface experts. The case in hand illustrates the advantage of data integration between Logging While Drilling (LWD) including Nuclear Magnetic Resonance (NMR), High-resolution image and Wireline (WL) formation testing and fluid sampling. The formation evaluation integration analysis results are used for characterizing the rock quality across multiple reservoir zones and to determine the optimum zones to be drilled horizontally and completed using the inflow control device (ICD) completion.
Appraisal and improvement of the lateral drainage area of the reservoir are the main objective of the field development strategy, by drilling pilot holes to evaluate Cretaceous carbonate layers then targeting the optimum zone for lateral side-track based on the provided data. Therefore, the plan has initiated by deploying the advanced LWD including the Gamma Ray, propagation Resistivity, Neutron, Density and Micro-Resistivity imaging along with NMR to acquire all the needed data for formation evaluation and reservoir characterization. The formation pressure testing and sampling device has been deployed using Wireline (WL) for the comprehensive evaluation of subsurface resources.
In this study, the reservoir petrophysical properties including total, effective porosity, permeability, movable fluid and bound water volume provided from NMR played an important role for the reservoir characterization and enhancement of the final formation evaluation results, based on the NMR results the formation testing / sampling points were decided. The Micro-Resistivity image revealed various types of geological features such as the conductive, enhanced, resistive fractures and different bedding plane in addition to different geological textures were interpreted along the well trajectory and was used to fine tune the formation testing and fluid sampling zones. The fluid mobility analysis from formation testing is used to calibrate the NMR permeability index along with the comparison with core data, also, the fluid characterization and sampling result are integrated with the rest of the data to formulate better reservoir understanding. The final integrated results from all the data and analysis used to identify the highest potential zones, therefore the side-track lateral drain was planned and geosteered based on the data integration results from the pilot hole. By extending the pilot hole understanding to the lateral section, the lateral section completed successfully across the optimum zones.
This paper provides insight into the integration of the Open Hole logging data from Logging While Drilling and Wireline to eliminate the uncertainty of the reservoir characterization and Formation Evaluation analysis to sustain and increase the oil production.