bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Multi-Frequency Characterization of Sandstone Rocks
Authors: San Roman Alerigi Damian , Alvarez Jose Oliverio , Batarseh Sameeh , Al-Khaldi Bander
Published: November 2020

The ultimate goal of our work is to develop an electromagnetic multi-frequency integrated system to enable real-time and in-situ characterization during high power electromagnetic (HPEM) operations, e.g. logging while lasing (LWL) or logging while microwaving (LWW). The sensing platform should provide information about the environment, the substrate, the near-wellbore (ahead of the beam), and the process. Real-time sensing is necessary because HPEM operations are faster and avail more control to target the electromagnetic energy to attain a specific purpose. Thus, the sensing systems must be compatible and operate simultaneously with HPEM radiation. In this context, electromagnetic multi-frequency characterization could provide valuable information to evaluate the near-wellbore volume, fluids, and the environment. This paper introduces essential spectrometry components and analytics.

The experiments reported focus on the characterization of tight-sandstones rocks from different reservoirs. The characterization process used various tools: microwave and radio-frequency resonators, Fourier-transform infrared reflectometry (FTIR), porosimetry, and sonic reflectometry. Five resonances were measured using a custom-made microwave resonator with measurements at frequencies between 980 MHz – 3.5 GHz. As this comprehensive characterization evolves, it will use machine-learning algorithms to display the correlation between the electromagnetic response of the material and other rock properties; e.g., clay content, saturation, porosity, and mechanical properties.

FTIR and low-frequency impedance enable the characterization at surface and bulk volume, respectively. The experimental results confirm the correlation between some chemical, mechanical, and electromagnetic properties of materials. These relations could be used to derive clay content, total organic content, maturity, saturation, among other material properties. The results of this characterization contribute to the growing database of electromagnetic properties of rocks under different configurations. The expectation is that this information can be coupled with statistical and machine learning algorithms to build edge neural engines for subsurface characterization and HPEM tools.

Electromagnetic tools and methods for subsurface allow characterizing the formation with high accuracy and resolution. The combined results and algorithms could enable the estimation of rock type, porosity, saturation, and other properties while conducting HPEM operations. The information provided could eventually enable automated systems and logging-while-lasing. This work presents the foundational blocks to achieve this and guide the development of subsurface laser technologies.
A New Technique for Common EoS Model Development for Multiple Reservoir Fluids with Gas Injection
Authors: Aoki Takeshi , Furuta Tetsuro , Leekumjorn Sukit , Azeem Shaikh Jawad , Schou Pedersen Karen , Alobeidli Abdulla , Mogensen Kristian
Published: November 2020

A new technique is presented for the development of a common equation of state (EoS) model for multiple petroleum reservoir fluid compositions counting heavy gas condensates, near-critical fluids and black oils. The basic idea is that the phase behavior of a reservoir fluid to a large extent is determined by its critical point. This is not only the case at near-critical conditions. An EoS model matching the critical point of a petroleum reservoir fluid will in general also provide a good match of routine and EOR PVT data for both gas condensates and black oils. A measured critical point will seldom be available, but can be estimated from other available PVT data. The new technique is exemplified through the development of a 9-component common EoS model for 21 H2S rich reservoir fluids, 3 near-critical fluids, and 18 black oils. All 21 fluids had routine PVT data and 11 had solubility swelling data. Using the available PVT data, an approximate critical point was determined for each reservoir fluid composition. The critical point and the saturation point pressure at the reservoir temperature were the only data tuned to when developing the common EoS model. With this simplified method, the time spent on developing a common EoS model for a large number of fluids can be significantly reduced. The method provided a good match of the PVT data for all 21 reservoir fluids including the shift from bubble point to dew point on the swelling curve for the black oil compositions that had solubility swelling data.
Three-Phase Numerical Solution for Jet Pumps Applied to a Large Oilfield
Authors: Merrill Robert , Shankar Vivek , Chapman Thomas
Published: November 2020

The Mangala oil field in Barmer, Rajasthan is one of the largest onshore fields in India. The field is under an active polymer flood with over 300 wells injecting >400 kbwpd. Jet pumps have been the main mode of artificial lift for the field since production commenced in 2009. The jet pumps are "reverse circulation" type with the power fluid (water), pumped down the annulus. Despite their low efficiency, jet pumps are ideal for Mangala because heated water is used for the circulation fluid which avoids wax deposition in well tubulars.

Simple performance curves, often used in commercial packages, work well under two phase liquid only or low GOR conditions. With high gas volume fractions and higher drawdowns, the additional complexity of critical flow is introduced; this occurs when the fluid velocity reaches the speed of sound within the jet pump. These conditions are often encountered in the Mangala pumps, and many jet pump solutions become grossly inaccurate when used for these conditions.

A field-wide network model is being used for production optimization; the model employs coupled power fluid and production network systems. Given the size of the power fluid network, an accurate and fast method for jet pump performance under three phase conditions is a prerequisite to the modelling of both well behavior and jet pump sizing.

This paper presents a full solution of the fundamental jet pump equations, which are based on Bernoulli’s principle and the conservation of momentum. The method follows the classic solution presented by Cunningham (1970, 1995), which made simplifying assumptions of perfectly incompressible liquids and ideal gases. The proposed technique eliminates these simplifications, making the solution applicable for the situations commonly encountered in the Mangala field.

Existing jet pump models for Mangala are based on spreadsheet solutions. These solutions are slow and are practical only for the sizing of individual jet pumps. The new model presented in this paper solves the fundamental equations using numerical techniques and properly accounts for high compressibility fluids, multiphase flow and critical flow. The model was written in Python, which is an object-oriented language well suited to modeling the individual components of the system. The model solves the integrals involved in the revised Cunningham equations, using Newton-Raphson techniques to solve the equations iteratively. The model is used to generate complete (and complex) VLP curves in standard formats which can then be used directly with network models. The new model executes 25-100 times faster than the previous spreadsheet model.
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

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 (<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

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

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

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

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

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

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

Many 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

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.
Fit for Purpose Completion Design & Work Flow Process to Improve Accessibility & Conformance Control Onshore Field Abu Dhabi
Authors: Ladmia Abdelhak , Al Katheeri Abdulla Bakheet , Edmonstone Graham F. , Al Saadi Sami , Deblais Eric , Ghosh Dipankar , Al Hosani Fahad Mustfa , Abdulhalim Abdallah , Abdulsallam Fouad , Al-Khatib Haitham Mohamed , Mcneilly Kevin Dean
Published: November 2020

ADNOC Group of Companies are embarking on drilling many of development wells in the upcoming years to achieve the forecasted production mandates. Therefore, the wells completion should be designed to cater for the following reservoir requirements as per as per company expectations: ▪Full accessibility (to TD) for production logging and stimulation.▪Flexibility for conformance control.▪Defined production allocation.▪Fit for purpose completion that can absorb the change in reservoir dynamics throughout well life.

The current practice of completing the horizontal wells as open hole "bare-foot" completion and geosteering practice might save some short-term cost, but it may impact the hole accessibility and cost more in the long term.

Horizontal well application is one of the key development strategies that will improve the sweeping efficiency and recovery to minimize field development cost by reducing well counts. Longer horizontal require lower completion liners to meet the objectives of the well. Full multi- disciplinary input are needed to properly install an effective lower completion, but robust design often brings strong performance. This paper describes the outcomes of accessibility, geo-steering practices evaluation, fundamental criteria and workflow for selecting the most suitable lower completion design for new and sidetracked wells to achieve ADNOC's production mandates while complying with completion requirements and adhering to reservoir management guidelines.

The objective is to optimize the procedures as per ADNOC guidelines in designin, planning and deploying horizontal wells including the Well Post Rig Operation & Monitoring Process, Lessons Learned and assuring full accessibility while meeting integrity and quality of the development wells for future prognosis.

The main drivers and objectives for running lower completion are listed below: ▪Enabled to maximize reservoir contact aiming for cost and production optimizations▪Full Accessibility to allow PLTs, Stimulation and re-stimulations.▪Compartmentalized lower completion for remedial actions▪Achieve uniform flow conformance utilizing ICVs & ICDs▪Mitigate and manage water production
Implementation of Tracer-Based Production Logging Technology for 3 Phases Inflow Profiling in Offshore Extended Reach Drilled Wells
Authors: Ovchinnikov Kirill , Malyavko Evgeny , Husein Nadir , Gorbokonenko Oksana , Buzin Pavel , Gazizov Ruslan
Published: November 2020

In the context of high hydrocarbon price volatility on the global market and worsening resource structure an increasingly greater attention is paid to the search of effective and economically sound tools for production and exploitation process management and control, including in horizontal and directional wells.

Traditional approaches to horizontal well surveys by PLT using CT come with low industrial safety of work performance, high cost and, therefore, limitation in wide use. One of the alternative approaches is to use well marker monitoring system enabling to obtain unlimited information about inflow profile and composition along a horizontal well without interventions on a long-term basis.

This paper describes an approach of placing the marker monitoring systems as a part of completions and carrying out 3-phase monitoring for oil, water and gas, including off-shore project conditions.
Case Study - Comparative Structural Analyses for Dynamically Sensitive Platforms
Authors: Pai Arun , Samant Sachin , Karuppusamy Kandasamy , Kamal Faris , Takieddine Oussama
Published: November 2020

Low frequency platforms with a natural period above 3.0 seconds are sensitive to the wave induced forces. The present paper discusses the available methods in offshore industry to carry out the Extreme Wave Analysis (also called as Dynamic Inplace Analysis). The paper also compares the results of these methods with the normal approach of amplifying the wave load using a factor called Dynamic Amplification Factor (DAF). It finally gives a threshold limit based on the platform period for the use of appropriate method of Extreme Wave Analyses.
The Green Revolution for Oil&Gas Using Battery-Less NB-IoT IIoT Devices Powered by Waste Heat for Process Maintenance
Authors: Aragones Ortiz Raul , Nicolas Alegret Roger , Oliver Malagelada Joan , Malet Munté Roger , Ferrer Ramis Carles , Comellas Vogel David , Voces Merayo Ramon
Published: November 2020

Our planet has a tremendous problem with the air pollution and climate change. The last study published by Jos Lelieveld et al. [1] remarks the high exposition risk of EU Citizens to suffer cardiovascular diseases due to our poisonous air quality, killing yearly 800.000 EU citizens and reducing 2.2 years our life expectancy. Additionally, the last report presented on the UN conference for climate change on 25th of September'19, the IPPC, sets that if the water temperature increases 2°C, the sea level will rise 42cm from this year to 2100 but. If it increases 3°C, the rise will be 84 cm.

Besides, it has been demonstrated that the carbon footprint associated with various human activities leads to a steady increase in global mean temperature. Most of the gases that human activity emits into the atmosphere are due to the industrial processes that require a lot of energy for the transformation of the raw materials. Furthermore, a large part of the energy consumed in the industry is dissipated in the form of heat, also called waste heat. As a clear example, in the EU27, it is estimated that more than 65% of the energy used in the energy-intensive industries (EII) industry is lost in form of waste heat, representing yearly the 21% the EU energy needs.

This paper presents new wireless and battery-less industrial Internet of Things (IIoT) devices powered by waste heat for measuring vibrations in rotative machines, called INDUEYE IIoT. These self-powered devices will help huge energy demanding industries (especially chemical, petrochemical, oil refineries, etc.) to become more environmentally friendly and profitable in their digitalization process towards Industry 4.0. Also, these new industrial sensing devices take benefit of the new long-range wireless protocols such as NB-IoT or LoRa that simply eliminate all the wireless infrastructure in the facility (and its associated costs). Additionally, the edge-computing concept is introduced in the INDUEYE IIoT device decreasing to 98% of the cloud computation, reducing in consequence, the cloud computing carbon footprint, and its associated fees.
The Method and Strategy to Optimize Well Density for Oilfield Development Projects in Technical Service Contract Framework
Authors: Li Changyong , He Juan , Zhang Yu , Dai Weihua , Pi Jian , Qi Chengxiang
Published: November 2020

This paper developed the method and strategies to optimize well density for oilfield development projects in Technical Service Contract (TSC) framework. The main TSC terms, including plateau production, capital investment, remuneration fee, etc., were summarized as the basis of well density optimization study. 8 calculation methods were recommended to estimate the technical reasonable well density which is fit for the plateau production requirements. Incorporating with the Shelkachov equation, which reveals the relationship between ultimate oil recovery factor and well density, an investment income analysis model, not taking into account the time value of money, was derived for TSC oilfield development projects. The well density values corresponding to the maximum and zero governmentWs profit were defined as government economic reasonable well density and government economic limit well density respectively. The influence of well density on contractor's profit was further investigated. Finally, the proposed method and investment income analysis model were used in a specific oilfield of the Middle East, which shows that the contractor's profit monotonically decreases with the augment of well density S within a certain range. The strategies, including enhancing oil recovery through drilling more wells and integrated operation with oilfield service companies, are recommended to the leader contractor of this TSC project.
Application of Techno-Economic Energy Intensity Ratios in the Oil and Gas Industry
Authors: Grassian David , Bahatem Mohammed
Published: November 2020

Net Energy Analysis (NEA) provides essential information and insight that can be used to support field level decision making by oil and gas owner/operators. It contains important information that compliments traditional field level analytical methods that take into account operational and capital expense (OPEX and CAPEX) estimates, discounted cash flows, production volumes and recoverable reserves. The objective of this research is to describe practical methods of conducting field-level NEA and demonstrate the benefits via thought-provoking hypothetical case studies.

One way to integrate NEA and economics is through the use of Energy Intensity Ratios (EIR) at the field level, which focuses on the ratio of the energy costs to the energy intensity of a field, two critical factors affecting both a field's economics and environmental impact. To begin with, this ratio is developed for a number of diverse hypothetical fields and provides an insightful comparison of the underlying economic drivers of different production schemes. Sensitivities on the underlying energetic drivers of a field are explored, taking into account the following recovery methods: (1) water flooding with downhole pumps for production; (2) water flooding with gas lift for production; and (3) steam injection with downhole pumps for production. Additionally, the EIR concept is applied to three small oil fields located in the Gulf of Thailand, with differing energy intensities and fuel sources. The three actual fields all employ water flooding, or water disposal, with downhole pumps for production, which makes for an interesting comparison with the first hypothetical case.

The field level EIR provides an interesting perspective on the economic drivers of the field and clearly differentiates between a production scheme in which the fuel is inexpensive, e.g. from unmarketable production fluids, and the energy intensity of production is low, to a field with higher fuel costs, e.g. imported power or liquid fuels, and a corresponding high energy intensity of production. This research also describes how deviations in operational expectations can have a substantial impact on the energetics, environmental impact and economics of an oil extraction development.
IoT in Offshore Operations: Business Implications and Opportunities
Authors: Ghamdi Ahmed , Amry Meshal
Published: November 2020

Objectives/Scope

The objective of this paper is to discuss the business implications brought upon by the Internet of Things (IoT) and the opportunities in increasing offshore operations safety, efficiency, and innovation.

Methods, Procedures, Process

Several operations in offshore production platforms are studied as to determine how can be transformed using IoT and other digital technologies to yield safer and efficient alternatives. The main processes covered in this paper cover well performance optimization, platform maintenance schedules, and means of minimizing human intervention by utilizing robots to perform the routine operations. The proposed initiative is to develop an automounts smart platform able to run safely and efficiently with minimal human intervention. Some of the sensors and actuators that are going to play a big part in this proposition include a wide range from bottom-hole sensors, artificial lift equipment, surface equipment, and up to the first separation stage.

Results, Observations, Conclusions

The paper aims to establish a roadmap for establishing digitally capable offshore production platforms to minimize human intervention, increase safety, and yield optimum production performance. We layout the roadmap considering the importance of how to ‘Add Value’ by utilizing the digital technology. Three main aspects discussed include how to optimize well performance through real-time choke performance utilizing by all the sensors upstream and downstream, and the actuators, like the choke valve. The integration of well flowing parameters with AI assisted systems for constant evaluation of well performance and abnormality detection is a major and feasible mean of increasing value of these assets. Once a sufficiently large set of data within the IoT umbrella is available for data analytics, then engineers can create innovative ways of handling day to day operations without minimal human intervention.

Novel/Additive Information

The paper expands on previous knowledge by specifically mentioning areas where integrating IoT is most feasible, with objective of improving offshore production operations to achieve a sustainable, profitable, and safe operation.
IAOM Modelling to Optimize New Brownfield Project
Authors: Alghafli Afra Hamad , Aladsani Amna Yaaqob , Al Kaabi Sultan Hamdan , Mcneilly Kevin Dean , Akhtar Mohamed Masud , Abujarad Nasser
Published: November 2020

An efficient well and facility surveillance and monitoring process is crucial in the current low-oil-price environment. Identification and mitigation of bottlenecks are essential for a coordinated system. This paper illustrates how the integrated asset modeling (IAM), with an effective work approach and cooperative work environment, is helping to optimize a super-giant brown field with new major facility integration. Real field-based case studies that focus on identifying opportunities in well performance improvements as well as debottlenecking will be discussed in this paper.

The objective of using IAM for the new surface facility project is to perform well and facility performance monitoring, establish efficient reservoir development approach, and asset performance optimization for short and long term production plans. This process includes modeling of wells, surface network, and facility structure which are integrated to various data sources in a production optimization platform. These models were used for evaluating several field-scenarios, confirming deliverability of planned and sustainable production, identifying bottlenecks, as wells as assisting engineers in understanding the interactions between different parts of the production system. In which the engineer is able to identify opportunities for production-system improvements.

The digital production optimization platform assists in the implementation of several automated workflows of performing wells and surface facility components for monitoring and surveillance. Where it also provides an approach for continuous work analysis process in which the representative models and scenarios are built and validated based on input information received from several data sources.

This paper demonstrates the current practice of using IAM for the entire surface and subsurface production system, which include evaluating and assessing the impact of the current facility constraints and limitations, identifying de-bottlenecking opportunities. It also focuses on the assessment of the major upgrade of the surface facility project in the field where dewatering systems on the current degassing stations (RDS) are added. It also shows the significance of data integration in which several quantitative and qualitative challenges pose a demand for establishing efficient work process in an integrated manner which involves various organization teams and disciplines.

This paper also sheds light on handling various challenges relevant to the utilization of IAM models in a digital framework that consist of intelligent workflows and diagnostic analytics. Utilizing true potential of IAM is a vital element for defining optimum production optimization strategy and in reducing the capex and opex investment. Which is a key in achieving the key business objective and KPI of the newly formed integrated facility project.