bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of Machine Learning-Assisted Fault Interpretation on Large Carbonate Field with Subtle Throws
Authors: Oke Mujeeb , Tilley Samuel , Manral Surender , Gacem Mohamed Tarek , Mawlod Arwa , Al Daghar Khadijah , Ben Jeddou Houcine , Mustapha Hussein , Li Cen 
Published: November 2020

Abstract
We illustrate novel application of Machine Learning (ML) assisted fault interpretation in the Middle East, interpreting complex fault structures with subtle throws in a large carbonate field onshore Abu Dhabi. Introduced as part of an integrated multi-disciplinary digital excellence initiative at ADNOC, ML-assisted fault interpretation seeks to overcome historic operational bottlenecks caused by traditional seismic interpretation methods which are slow, labour intensive, repetitive, and subjective. Core objectives for deploying ML-assisted fault interpretation were to reduce evaluation time, improve interpretation accuracy, and ensure integration across an intelligent evaluation ecosystem comprised of various disciplines. Envisaged gains from deploying ML-assisted fault interpretation methodology included effective and efficient utilization of multiple seismic datasets to drive rapid multi-scenario analysis, leading to better subsurface understanding within much shorter time frames.
Input data used of the project was standard amplitude volume with minimal user-end conditioning. PSTM time and PSDM depth seismic volumes were used in separate runs to confirm that applied ML technology is domain agnostic. The ML-Assisted workflow included: Generating a fault prediction cube based on user-supplied fault interpretation labels made on 6 training lines (&lt;0.8% of the available lines); Creation of fault planarity and azimuth cubes; Parameterization of automated extraction function; Extraction of segmented 3D fault pointsets; Creation of fault framework and fault sticks that can be integrated into traditional methods in seismic and geological modelling domains.
Despite limited fault displacement apparent on the seismic volumes, ML fault predictions were of high quality, closely adhering to the seismic response as guided by user-provided training samples. Advantages envisaged from use of ML-assisted interpretation technology in the project were fully realized as the technology enabled rapid extraction of complicated fault structures within a fraction of the time and effort previously taken using traditional means. Efficiency and precision gains from using ML-assisted fault interpretation presents benefits that single seismic volumes can be evaluated thoroughly, and multiple seismic datasets (e.g. various azimuthal volumes) can be evaluated consistently for multi-scenario analysis to reduce subsurface risk and inform better decisions at all phases of the E&P Asset lifecycle.
Unexpected High Water Saturation in a Mature Carbonate Reservoir : An Holistic Investigation
Authors: Thomas Tony , Singhal Sangeeta , Maire Flavien , Pandey Vikram , Al Hashmi Sara 
Published: November 2020

Abstract
This work is done in the context of a giant carbonate reservoir with 40 years of production/injection history. In this reservoir, the key heterogeneities that impact the production mechanisms (high permeability streaks, permeability contrast, intra dense and dual sub-zones of varying maturity) are well characterized, through major field reviews. However, in the recent years, unexpected high water saturation were encountered while drilling infill horizontal drains. An integrated multi-scale investigation was conducted to understand the potential mechanism of water movement within the lower sub-zone through an assimilation and interpretation of data and multi-disciplinary understanding. This work resulted in further optimization of remaining infill wells and the field development strategy.
The observed high water saturation signatures were investigated through integration and analysis of multi-disciplinary data, using data acquired in infill wells during the last three (3) years. First, the studied wells were categorized primarily based on their water cut, water saturation encountered and then mapped along with their data. Second, the different scenarios of possible water movement were developed chronologically. Third, a multi-scale integrated analysis (well level to reservoir level) and subsequent mapping was captured on a montage. The developed scenarios were validated with recent surveillance, monitoring data and corroborated with geological/geophysical understanding.
This comprehensive approach and results were corroborated with blind tests in terms of the dynamic behavior of the water. Faults and their characteristics were identified as a key element affecting this local dynamic behavior. Saturation logs showed high water saturation but were inconclusive on whether this water is mobile or not. This required integration with dynamic flow data from production logging and well tests. The dynamic data was integrated with the seismic and geological findings to ascertain whether the water presence is due to poor rock type, water migration through faults or water movement in the matrix. The findings were mapped and led to re-consider the well placements and expectations from infill wells while crossing faults. This enhancement of the reservoir understanding resulted in mitigation of risks in future producers which subsequently impacted the reservoir development strategy.
This multi-scale analysis over the reservoir provided an insight into the source of the unexpected high water saturation in the undrained lower sub-zones of the reservoir. Thus, revising the understanding of the field development strategy, well placement and related contingencies.
Application of Rock Physics and AVO Analysis to Improve Hydrocarbon Prediction; Case Study Based on the Nong Yao Oil Field in the Gulf of Thailand
Authors: Kaewprain Phansakorn , Ampaiwan Tianpan , Carter Raweewan 
Published: November 2020

Abstract
In hydrocarbon exploration, rock physics analysis plays a key role by connecting seismic data to rock properties. Analysis of rock physics data enables geophysicists to understand how fluid content affects the seismic response and what they should look for to improve the chance of finding hydrocarbons. In the Nong Yao oil field, the use of rock physics and AVO analysis was used to improve the hydrocarbon prediction process.
The preferred method starts with rock physics analysis of key wells. Fluid Replacement Modelling (FRM) is then performed across many wells in order to generate a predicted seismic response for different pore fluids (gas, oil and brine). The predicted AVO response is then calibrated against the actual AVO response from the seismic data from key wells in order to build a database. In the Nong Yao Field, over four hundred and fifty drilled data points from sixty-nine wells were utilized in the analysis. This database is analyzed in order to find the optimal combination of parameters for hydrocarbon prediction, which is then used to improve hydrocarbon prediction for future near-field drilling candidates.
Near-field appraisal programs in the Nong Yao oil field are driven strongly by amplitudes and AVO, as rock physics analysis has shown that sands and shale lithologies can be easily discriminated based on acoustic impedance. Fluid prediction is more difficult based on acoustic impedance alone, as other factors such as variable sand thickness and seismic data quality mean that there is significant overlap between hydrocarbon and wet sands. Rock physics analysis has shown that AVO behavior can be included to provide better separation between hydrocarbon sands and wet sands.
AVO signatures from all the data points are then analyzed using intercept vs gradient cross-plots. A background wet trend is defined with the clear observation that increasing distance from the background wet trend correlates to increasing chance of hydrocarbon fill. Data are categorized into weak, moderate and strong AVO response based upon their distance from the background wet trend and then this is used to modify the chance of success of near-field appraisal drilling targets utilizing conditional probability. This results in an increased chance of success of up to 20% in a strong AVO supported target and around 10% in a moderate AVO supported target. Targets are then quantitatively high-graded in an appraisal portfolio.
Success Cases of Identification of Field Development Opportunities Using Rate Transient Analysis RTA In Talara Basin - Peru
Authors: Sabrera Chia Tobias , Dextre Rubina Javier , Galloso Terrones Jorge 
Published: November 2020

Abstract
Talara Basin, located along the northwestern coastline of Peru, has been developed for more than 130 years, having as main geological feature the high structural complexity due to the massive faulting that the formations are subjected. Being a relevant challenge to adjust the static and dynamic characterization from the reservoirs. As the area is in an advanced development state it is crucial a detailed evaluation when identifying additional development opportunities such as the location of new wells and potential workover jobs. This paper summarizes the application of the rate transient analysis (RTA) methodology in the reservoirs from the Talara basin as a versatile tool for the dynamic characterization and identification of development opportunities with applied cases in the Salina formation of Block XIIIA.
The applied workflow begins with the collection of low frequency records, such as flow rates, wellhead and bottomhole pressures which are the basis input for the RTA models. Using the available analytical models, the dynamic characterization of the reservoir is achieved, obtaining reservoirs parameters like effective permeability, drainage radius and current reservoir pressure. These data were validated and calibrated with well tests and PTA (pressure transient analysis), obtaining very good adjustments with the RTA models. The second step is the creation of numerical RTA models at the structural block level (limited by local faults), using well information, geology, and reservoir data. Sensitivities are made to both the reservoir parameters and the transmissibility of the faults (blocks with original pressure were identified), achieving the history match of the blocks and obtaining maps of current reservoir pressure for each unit flow.
New wells and workover jobs were identified and executed using the information obtained from the RTA numerical models, resulting successful jobs, and obtaining a very good match to the prediction of productive performance of wells. In addition, the current reservoir pressure maps were validated when measuring the formation pressure after drilling using wireline formation tests and mobility and permeability values per flow unit were obtained. In the case of workover jobs, static pressure calculations were made with the information obtained from minifrac (using After Closure Analysis) in all cases the obtained values of current pressure resulted in adjustments of less than 3% of error.
According to the cases presented in this paper the feasibility and versatility of RTA models in the predictability and reproduction of the behavior of the reservoirs of Talara Basin are demonstrated, helping to identifying business opportunities, mitigating depletion risks, and making better decisions for the well completion
Prediction of Well Production Event Using Machine Learning Algorithms
Authors: Alatrach Yara , Mata Carlos , Shoeibi Omrani Pejman , Saputelli Luigi , Narayanan Ram , Hamdan Mohammad 
Published: November 2020

Abstract
In this paper, a new approach was identified and tested to detect abnormal events in producing wells when a labeled dataset is unavailable or the number of instances are below 10% and are insufficient for conventional modelling methods. Autoencoders (AE), a type of unsupervised learning, are trained to learn normal behavior by trying to reconstruct the input data that is fed into the model. When run in prediction mode, low reconstruction errors are classified as Normal behavior whilst higher errors are classified as anomalous behavior. Different model structures were tested. An average accuracy of 94% with a precision and recall rate of 70% was achieved using a 6-Layered AE-NN model. The results of the models created show encouraging results and can help detect events and notify engineers when the well is deviates from expected behavior.
An Adaptive CPR-AMG Based Linear Solver for Simulating Geometrically Complicated and Fractured Reservoirs
Authors: Mohajeri Sina , Eslahi Reza , Bakhtiari Maryam , Alizadeh Ali , Zeinali Mostafa , Madani Mohammad , Rajabi Hamed , Sharifi Ebrahim , Mortezazadeh Emad , Mahdavifar Yasser 
Published: November 2020

Abstract
A great deal of computational power and time is necessary for simulating highly heterogeneous fractured reservoirs with complex geometry; the efficiency of these computations is a major subject especially in large-scale heterogeneous and fractured reservoirs. So, simulating large scale, complex and fractured reservoirs with both minimum time and maximum accuracy is the scope of current work. The BiCG-Stabilized solver preconditioned by CPR-AAMG has been developed to achieve acceptable results of high efficiency and robustness for large heterogeneous fractured Black-Oil models. The solver's efficiency is demonstrated in an Iranian fractured field model with heterogeneity. As an observation, the NF preconditioner which is embedded in ECLIPSE has low efficiency in simulating giant fractured reservoirs. On the other hand, the advantageous efficiency of the proposed CPR-AAMG preconditioning technique increases with the complexity of the model. The findings indicated that the CPR based preconditioners have better performance than classical ILU based preconditioners according to the number of linear solver iterations in RETINA. CPR-ILU0 is the most efficient preconditioner, based on speed and calculation cost (numbers of linear solver iterations). RETINA can take larger time steps in comparison with ECLIPSE, and therefore has better elapsed time. Comparison with the results of other preconditioners developed in ECLIPSE approved that the developed CPR-ILU0 in RETINA is robust. The main conclusion would be that RETINA has an exceptional and stable performance for simulating highly heterogeneous fractured models. The novelty of the proposed CPR-AAMG preconditioner is the ability to solve three dimensional black-oil models of giant, complex and fractured reservoirs with reduction in time and required computational power.
An Automated Reservoir Surveillance Planning, Tracking and Optimizing Approach to Enhance Reservoir Management Efficiency in a Giant Offshore Brown Field
Authors: Zhao Wenyang , Al Kindi Salem Ali , Al Shehhi Omar Yousef , Al-Feky Mohamed Helmy , Sugawara Yuki 
Published: November 2020

Abstract
Data acquisition is the most fundamental cornerstone to a sound reservoir management and a data driven culture in field development. The quantity and quality of data are undoubtedly essential. The challenges of obtaining timely, accurate and trustworthy data are much more significant in brown fields, where advanced data gathering approaches are generally not available. Efficient planning, tracking and optimizing are mandatory to maximize value of the limited resources. The routine reservoir surveillance activities include flow testing, bottom-hole pressure survey, case-hole logging, and sampling. An automated tool has been developed to plan the annual requirements, track the progress and optimize the resource utilization. As a rule-based model, the approach determines each type of monitoring activities based on the analysis of historical surveillance data and pre-set guidelines. The preliminary planning results are automatically distributed to the responsible engineers for final confirmation and adjustments. The completed tests are timely gathered for analyzing, tracking and reprioritizing future activities.
The approach is consist of two main parts: planning part and tracking & optimizing part. During planning part, a complete well list is generated automatically and quality controlled by both automatic and manual examination. Latest flow tests, pressure surveys, and logging history are gathered from database and analyzed to plan and prioritize the requirements for each string. The requirements are determined based on reservoir management guidelines. Priorities for each type of activities are set up based on last survey timing and well status. The plan is then transferred to the tracking and optimizing function as a reference to guide and optimize operation. The tracking and optimizing part enables direct link to the raw data of flow tests and pressure survey results. A dashboard is designed to report the progress on flexible frequencies. Lessons learned are registered by engineers right after validation. Opportunities to do rigless intervention or rig workover can be identified or confirmed efficiently. The timely identification of opportunities could usually save huge costs or generate enormous gain. With this tailored in-house innovated tool, this automated approach has significantly improved the efficiency of reservoir monitoring planning and optimizing in such a giant offshore brown field, which has more than 600 strings. The automatic distribution and gathering have dramatically reduced time and effort comparing to the conventional way. The efficient approach could consolidate the foundation of a data driven culture.
Improving Diagnosis and Feedback for Water Conformance Interventions in Horizontal Wells: Case Study Onshore Abu Dhabi
Authors: Sookram Neil , Mohammed Alblooshi Alya Abdulla , Prahawinarto Bambang , Noordin Firdaus Bin , Afeefi Baraka Said , Albuali Abdullah Yaseen , Jabbour George , Dasgupta Suvodip 
Published: November 2020

AbstractMany wells in the Bu Hasa field in Abu Dhabi are completed with near-horizontal or horizontal laterals in the consolidated Lower Cretaceous carbonate formations. These slim barefoot completions allow improved production and drainage from the good permeability formations in the field, however surface production measurements do not tell the full story on the contributing zones downhole.To improve the reservoir management many production logs with specialized logging technology developed for horizontal wells with multiphase flow, are undertaken in the field. This paper presents a case study of one of these wells that was showing less than expected oil production along with a significant but abnormal water cut. Production logging was done to acquire an inflow profile along the lateral, and a pulsed neutron log for formation fluid saturation was also acquired to diagnose any increased water saturation zones.The production profile showed that less than 40% of the open hole lateral length was contributing and that most of the water was coming from a specific section of the well. The formation fluid saturation profile confirmed good oil saturation across the whole lateral as was expected but there was a clearly higher water saturation across the water-producing section of the open hole. Corrective action was taken on the well after these logs with a completion change incorporating various Inflow Control Devices (ICDs) for the lateral section, and gas lift mandrels for improved lifting capability if required in the future. The lateral was then acid stimulated and put back on production.A second production log, again in combination with the pulsed neutron saturation log, was acquired to review the changes in the downhole inflow profile after the completion change. This profile showed that all sections of the lateral were now contributing effectively to the total oil production confirming the effectiveness of the new completion. Water was still coming from the higher water saturation zone that was again observed on the pulsed neutron saturation analysis, however it was apparent that the AICD was controlling the water entry as per design.Surface production measurements indicate reduced water cut and improved oil production confirming good success with the overall intervention. Currently water cut on the well is still slowly but continuously decreasing while the oil production is also improving. Continued monitoring will provide additional feedback for future water conformance operations in the field to assist in improving recovery factor from the reservoir.
More Oil and Less Water: Autonomous Inflow Control Devices AICD in New and Old Producers in Heavy Oil Fields from South of Oman
Authors: Al Harrasi Ameera , Gokmen Melih , Abri Ibrahim , Al-Jumah Ali , Harthy Khalfan , Busaidi Hamood , Buwaiqi Salim , Al-Joumaa Jihad 
Published: November 2020

Abstract
This paper summarizes the integrated technical learnings from the successful application of a an Autonomous Inflow Control Device (AICD) in new long horizontal wells producing heavy oil in multiple fields and show the result of extending this technology to the old producers in the field. It details the facts and observations that multidisciplinary authors have captured.
The AICD was completed in more than 40 new oil wells in multiple fields. It comprises of mechanical devices installed with the sand face completion, which react in real time to the properties of the flowing fluids, decreasing/delaying the water influx from high productivity zones, promoting increased oil production from other compartments of the formation, therefore, equalizing the drawdown along the horizontal section of the well and performing a dynamic water shut-off operation. The great successful result from new oil wells with this technology AICD opened the door to do few trials in existing producers.
The AICD-completed wells showed initial water cut in the range 1% to 2%. Which had reduced significantly in comparison to nearby analogues. The initial net oil rate resulted to be more than 2 times of the expected one, with an acceleration of ~10,000 bbls of net oil during the first month. After the initial production period, the technology is still delaying the aggressive water cut development usually observed in these fields, having provided 2 times the expected net oil rate during the first 3 months, with an acceleration of approximately 20,000 bbls of net oil over this period. It has been concluded that the application of the technology is successful and already deployed as a baseline in all future horizontal wells drilled. Also this technology was tried in old producer wells and already 15 wells completed with AICD completion which shows good reduction in gross/water and increase in oil production in some of the wells by more than 30%. With these successful results, 35 wells from existing producers were planned to be completed by AICD in 2020 from multiple fields.
The authors have summarized the results from the AICD deployment and presented it as a practice worth replication in a similar heavy oil environment, due its many benefits in optimizing field development. AICD in new and old wells will be a game changer in all filed to reduce the water and increase the oil.