bluuebunny/crossref_metadata_2025 huggingface dataset:
Policy Incentives to Scale up Green Energy Supply
Authors: Diaz Suhail , Al Hammadi Nasser , Prakash Shashi , Martto Joshva , Baobaid Omar
Published: October 2022

Satisfying the rising energy demands across the globe and limiting the environmental impact are two intertwining issues affecting stakeholders in the 21st century. This resulted in many policy incentives to promote eco-friendly green energy utilization together with other conservation measures. In this regard, the objective of the study is to investigate the introduction of business decisions that enhance the likelihood of companies adopting green energy sources, leading to reduced carbon emissions. It also intends to investigate various decarbonization strategies, which may have implications for profitability. Ultimately, it aims to underscore environmental preservation as a critical aspect of sustainable business practice.

The methodology entails an analysis of existing literature focusing on green energy policies and measures for the promotion of green energy. The study uses the survey method to investigate articles and perspectives related to the subject in the UAE. Observations will be made to determine the most effective policy and regulatory direction in areas of green energy and sustainability. Similarly, the process entails an examination of how to design a green energy policy with economic and environmental factors in mind and, through the analysis, the methods include an evaluation of different incentives.

The study underscored the importance of establishing energy policies and putting in place incentives for stakeholders, including businesses and end-users to embrace green energy production, supply, and usage. Key incentives identified include favorable government policies, rising costs of fossil fuels, and the need to reduce emissions levels globally. Notably, despite the high initial cost of green energy, the investigation noted substantial future financial savings along with protecting the environment and introducing the social cost of carbon (SCC) in the analysis. Therefore, standardization of green energy costs would contribute to the adoption of green energy sources. Above all, the rising cost of conventional energy makes it imperative for governments and organizations to adopt green energy.

Despite many studies on green energy, this paper presents novel ideas as it focuses on incentives for production costs. It outlines how to lower the cost of producing green energy to the same standard as conventional sources increasing its capacity compared with historical patterns. The incentives under discussion also underscore energy efficiency by making it a cheaper and cost-competitive alternative, and technology comes out as a crucial ground leveler in achieving cost-effectiveness and efficiency. For example, improvement of storage would be vital in storing power for extended periods once harnessed or fuel transitioning in transportation. Overall, government incentives and interventions in areas of funding, research, and development would mitigate existing challenges and contribute to the long-term development of green energy.
Best Practices to Achieve Optimization for Large Scale Offshore Carbon Capture & Storage Facilities
Authors: Zakaria Mohd Uzair , Muhamad Suhana , Zainalabidin Muhammad Aizuddin , A Rahman Aina Suharny , Narayanan Ashwin Kumar , Wan Hashim Wan Mahsuri , Nik Omar Nik Fauziah
Published: October 2022

Carbon Capture and Storage (CCS) is a key enabler for carbon emission efforts for a more sustainable future while fulfilling energy demand. However, CCS, especially when implemented offshore is costly and can be detrimental to project economics. A focused and methodological approach is critical in Front End Loading (FEL) to ensure optimization strategies are considered in the CCS development. The intent of this paper is to share the journey and knowledge gained throughout the front-end loading process in developing a large-scale offshore CCS project.

The success of the project lies in the effective management of integrated subsurface-surface decisions, collaboration with stakeholders, and commercial arrangements. Specific workflow which identified and screened CO2 storage location, volume, integrity, and containment of the injected CO2 over time was developed. It integrates key decisions such as handling low permeate stream, EOS selection, maximizing hydrocarbon recovery and pressure boosting technology concept. This was achieved via a comprehensive technology screening and selection method, evaluating its impact to the facilities design and injection wells, which ultimately affects the economic value of the development. A stakeholder management plan inclusive of technology providers and CCS experts, project execution delivery, commercial and Health, Safety and Environment (HSE) was also developed to ensure successful alignment for project progress and to protect the economic viability.

Structured evaluation and methodology enabled critical decisions and proposals to be properly assessed and eventually worked on to optimize the CCS facility. Among the optimizations identified includes an increased permeate pressure from feed source, reduction in surface injection pressure, identified optimum operating conditions, using the right EOS and selecting the highest value technology. Close collaboration between surface, subsurface and technology providers proved to be beneficial in crafting optimized CCS development. It is also important to ensure all required activities during FEL are executed accordingly and with prioritization of decisions to allow proper allocation of resources and to identify and mitigate any risk and uncertainties for the success of this large-scale offshore CCS.

More CCS projects are envisioned globally as an enabler to develop high contaminant fields and minimize Green House Gas (GHG) emission. Validated learnings and progressive experiences gained from this project will play a pivotal role and can serve as reference to other CCS projects.
Journey to Zero Lifting & Minimum Congestion for Rigless Activities in Block 61 bp Oman
Authors: Al Shidhani Taimur , Al Harrasi Sultan , Al Darai Asma , Al Sulaimani Ishaq , Al Juma Marwa , Najwani Mohamed , Al Fadhli Khalid , Al Abri Sultan
Published: October 2022

Rig-less activities, such as well-work, stimulation and testing (S&T) in bp Block 61, in Oman, often include rig-ups that typically involve lifting wide and heavy loads. These activities, if not mitigated against, can pose significant safety risks, such as dropped objects and suspended loads. The team's main safety objective was to eliminate as many risks as possible while executing these activities. Therefore, intensive focus was placed on the well testing package, as it is present in most of routine rig less well operations in Block 61. As a result, a major improvement to the package design was identified and implemented in Q2 2021, by "trailerizing" routine intervention resources, including well testing equipment and service camp.

Furthermore, at the start of the rig less journey in Block 61, each service provider supplied their own logistical resources. These resources included cranes, forklifts, flat-bed trucks and prime movers. However, this often led to multiple unnecessary vehicles on site, introducing the risk of congestion on the well site. Adding to that, having non-necessary vehicles previously caused collision incidents. To mitigate said risks, the team introduced the logistic hub concept, where each service provider utilized the relevant vehicles whenever required, instead of supplying their own. This initiative had further reduced congestion on site and ultimately lowered the probability of collisions and incidents.

Consequently, the new design resulted in 85% reduction of lifting activities. This led to minimal utilization of cranes and forklifts, which resulted in lower fuel consumption and ultimately lower indirect carbon emissions. Operational and capital efficiency were also improved as rig up / rig down durations have been approximately halved, and minimum time would be wasted on waiting for a forklift or crane. Due to the optimization of the logistic resources, there are no longer any idle vehicles on site, which reduced the emissions, as well as decreased the third-party costs associated to logistics. The unique aspect of this initiative is that a single supplier provides all required vehicles to the site as per demand directly with no commitment necessary. Having a single supplier provided stronger control of work in terms of lifting plans and procedures.

The rigid bp safety process guidelines ensured that all challenges and optimization opportunities were fulfilled in a safe manner. The purpose of this paper is to detail how the team pushed the technical envelope to introduce such initiatives and share the journey entailing the improvements made to achieve an inherently safer environment.
Game Changer in Subsurface Flare Reduction
Authors: Al Rahbi Moosa Salim , Al Sidairi Nada Abdullah , Al Ghafri Amal Mohammed , Al Ismaili Sultan Ahmed , Al Rashidi Ahmed Sulaiman , Awan Kamran Fahmeed
Published: October 2022

In the fast-changing world of energy transition, The Subsurface (SS) Flare reduction project in the Gas Directorate (GD) of Petroleum Development of Oman (PDO) was kicked off beginning of 2021 to ensure continuity in growing our business and generating revenue while reducing the carbon footprint of our operations. The two main value drivers of this project were firstly to minimize HSE impact and reduce GHG emissions, in line with PDO goal towards net zero by 2050, and secondly to maximize hydrocarbon recovery. This is a first of a kind project in PDO as the GD is leading the way towards addressing subsurface flaring. We started the journey by mapping out the different flare contributors (post-frac, well testing, Flow Back Loop (FBL) units and Halite Clean out), quantifying their impact and identifying the big actors. Then, we worked with the different teams from Engineering, Well Services and Operations to build a 5-year work plan with a clear roadmap to reduce subsurface flaring by 60% in 5 years. In the first year (2021), we managed to reduce SS flaring by 37%. This reduction was accomplished by introducing two efficiency improvements which included a successful Flareless Halite Cleanout trail with a full-scale implementation plan, and the utilization of test separators in line with SMS units to verify the flared figures. This resulted in a 50% correction factor to the data on hand. Going forward, the focus will be on maturing the new technologies that will further reduce SS flare such as Green Completion, Well Head Compression (WHC) units, mobile flare gas recovery, etc. Given the complex nature of this project and the multidisciplinary efforts from Petroleum Engineers, Operations, Engineering, Well Services and New Technology, constructing a successful working plan to address this issue required effective collaboration and thinking outside of the box to find innovative solutions. As a result, we constructed a funnel of efficiency opportunities with a clear timeline including Green Completion, WHC, pre frac hook up, and mobile flare gas recovery units. Additionally organizational tools for enhancing efficiency were applied such as PPS (practical problem solving) and Goal Deployment methodologies.

Such energy efficiency projects that reduce the GHG emissions with a streamlined process and identified involved stakeholders, help to better position the organizations to tackle the climate challenges. Moreover, they help to establish a better understanding of the current impact on climate and keeps an open eye for any new technology opportunity that can be materialized to reduce or eliminate GHG emissions. (Robinson & I. Russo, 2013)
Effective Front-End Loading Management - Most Occurred Flaws in FEL Execution
Authors: Manokaran Padmanaban , Al Rumaithi Fahad , Adi Mohamad Bara , Al Suwaidi Saeed Khalfan
Published: October 2022

Project Management team aims to strive for 100% success all the time. it's no secret that sometimes projects fail due to various reasons. But a project failure doesn't have to be the end of the story. Project failures can often offer valuable lessons for future project planning.

Given the magnitude of investment observed in an Upstream Projects, it is necessary to adopt appropriate methods and processes to ensure the economic viability of the projects, as well as to effectively control the fulfilment of scope, schedule, quality and risk mitigation. The oil and gas industry has consistently used the combination of both methodologies of the PMI and IPA in the development of major projects, with particular attention on the front-end loading methodology (FEL), which combines an approach of so-called "rolling wave planning", with a vision of technical and cost integration in the light of other Project execution techniques.

Certainly, most of Capital projects had fulfilled all the mandatory Front-End Loading (FEL) requirements set by an organization. However, at sanction gate only very few projects are allowed to proceed for execution, and rest are directed to redo or retrofit the FEL's. This Paper aims to capture the most occurred flaws in FEL execution of Upstream Oil & Gas Projects as below: Over Design & Gold PlatingIncomplete Technology SelectionPoor definition of Brownfield Works & Lack of Interface ManagementUnderstating Infrastructure RequirementsLow Stakeholder EngagementUnresolved Risks & UncertaintiesEstimating ErrorsOverly Optimistic Economics

In addition, it also presents recommendations to improve the FEL execution and minimize the potential design changes during Project execution. It can be seen clearly all the above issues are often occurring in most of the Capital projects during FEL execution. These findings can be viewed as a lesson learned approach for all Oil & Gas Upstream capital Projects. This will guide Owners Project Management team better understanding of FEL issues at upfront while developing new projects or executing the FEL phase within budget, on schedule with desired quality.
Improving Organizational Culture : High Reliability Organization
Authors: Al Ali Ali Ahmed Abdulla
Published: October 2022

Positive safety culture is key in a generative organisation where individuals anticipate what could potentially go wrong and accept bad news as opportunity for improvement. During the last several decades, numerous process safety related events have occurred impacting people's lives and companies' financial integrity. The underlying causes of the poor safety culture that resulted in major incidents is due to organisational and safety management failures, inadequate communication, and lack of cultural leadership responsibility.
Utilizing LNG Stochastic Model for Better Cost Predictability and Faster Decision Making During Front End Loading Stage
Authors: Rahmat Roszie Yanty , M Jais Ir Mashitah , Mohd Rahim Mohd Hamim , Azli Mohd Azwan , Veloo Sominidevi , Nawawi Noradnin Hafeeza
Published: October 2022

In the race towards greener energy, Liquified Natural Gas (LNG) stands out as an excellent alternative to reduce greenhouse gas emissions and help combat global warming. Over the years, the LNG cost has become increasingly competitive. With the proposed aspirational liquefaction capacity of 892.4 Million Ton per Annum (MTPA) against existing capacity of 452.9 MTPA, as February 2021 in International Gas Union (IGU) 2021 World LNG Report, the future of LNG market is becoming more and more robust, and cost competitiveness with faster decision-making process, is the key success factor to realize it. This paper will focus more about the process in developing this stochastic cost model encompassing various technologies to assist the project in front end loading stage to evaluate the proposed concept of the LNG plant and provide the sensitivity to the overall plant investment cost. This model utilizes the technical input such as plant capacity and number of trains, gas composition, various concepts of technology selection, project specific requirements, location and propose offsite facilities concept, to generate the high-level deterministic cost estimate. Compilation of technical data from completed LNG project from all over the world, alongside with high level process simulation for each process unit, are utilized as the backbones of the model simulation. Based on that, it will be integrated with the normalized cost data, to reflect the current market. This LNG cost model is tested and benchmarked technically and commercially against completed LNG projects, both internal and external company, with inclusion of one's corporate memories, to increase the predictability of the model. Post internal and external benchmarking analysis, the accuracy of the model is within the anticipated range. Utilization of this model will provide simplified concept select process, thus minimizing the front-end loading duration and enable agile engineering for technical and commercial evaluation.
Industrial Facility Top-Down Green House Gas Emissions Quantification Using in-House Gas Analyzer AUSEA Deployed by Drone
Authors: Donnat Ludovic , Huet Nicolas
Published: October 2022

The company became a multi-energy company in 2021. A new name for a new ambition to become a major player in the energy transition, engaged towards getting to net zero by 2050, together with society.

This vision is based on measurable objectives to reduce the company greenhouse gas emissions in the short (2025), medium (2030) and long (2050) term, covering own industrial operations (Scope 1+2) and the emissions generated by customers’ use of our energy products (Scope 3). The company affirms its ambitious target of a more than 30% reduction in greenhouse gas emissions related to sales of petroleum products (Scope 3 Oil) by 2030 compared to 2015

To that, the company has set phased targets for reducing methane emissions (50% from 2020 levels by 2025 and 80% from 2020 levels by 2030), to move towards zero methane and an objective of less than 0.1 million cubic meters per day for routine gas flaring at operated assets by 2025, before eliminating flaring completely by 2030.
Assessment of Integrated MMV Plan for CCS Projects in Depleted Gas Reservoir and Saline Aquifer Offshore Malaysia: From 4D Time Lapse Seismic Perspective
Authors: Tiwari Pankaj Kumar , Das Debasis Priyadarshan , C Mat M Noor Fajarimi , Leite Renato Jordan , Chidambaram Prasanna
Published: October 2022

There are many gas fields associated with large amount of CO2 concentrations (>50 mol%) in offshore Malaysia. Development of the contaminated gas fields is only possible if a geologically safe storage site is identified for the storage of produced CO2. The critical component of CCS project field development plan is monitoring of the storage site for the long-term containment and conformance security. 4D time- lapse seismic is key in monitoring, measurement, and verification (MMV) plan as it can demonstrate the periodic movement of stored CO2 within storage reservoir. Time-lapse seismic response depends on several parameters ranging from reservoir rock properties, initial fluid and saturation of CO2 during injection period. This study focuses on the different characteristics on 4D seismic attributes for carbonate and clastic reservoirs and discusses to effectively utilize for MMV.

The time-lapse 4D seismic has been traditionally used to identify the potential threats associated with reservoirs with enhanced oil recovery plan. The application of 4D seismic would benefits CCS project for monitoring CO2 plume migration along existing or induced fractures. The study was conducted on identified CO2 storage sites in both depleted carbonate gas reservoir and clastic saline aquifer. It highlights two methods applied in the 4D AVO analysis, that quantifies the changes in CO2 saturation within reservoir during injection. The Aki and Richards' approximation were analyzed for the effect of changes in gas saturations on the seismic amplitudes and the Smith and Gidlow's approximation for the reflection coefficient relationship between the AVO gradient/intercept attributes and reservoir fluid/properties.

The application of 4D seismic would benefits CCS project for monitoring CO2 plume migration along existing or induced fractures. The study was conducted on identified CO2 storage sites in both depleted carbonate gas reservoir and clastic saline aquifer. It highlights two methods applied in the 4D AVO analysis, that quantifies the changes in CO2 saturation within reservoir during injection. The Aki and Richards' approximation were analyzed for the effect of changes in gas saturations on the seismic amplitudes and the Smith and Gidlow's approximation for the reflection coefficient relationship between the AVO gradient/intercept attributes and reservoir fluid/properties. This study focuses on the different characteristics on 4D seismic attributes for carbonate and clastic reservoirs and discusses to effectively utilize for MMV.
South Oman Cluster GHG and Carbon Footprint Reduction Journey - Achievements and Aspirations
Authors: Muthalaly Mathen , Kumar Kamlesh , Agarwal Pankaj , Mihajlov Rodoljub , Kindi Zeyana , Harthy Fatma , Quraini Aiman , Hadhrami Abdullah
Published: October 2022

South Oman Sour Cluster facilities have historically recorded peak total Green House Gas Emission (GHGE) of ~2.1 million tons CO2e in 2019 representing largest volumes within Petroleum Development Oman (PDO). The key contributing factors were lower system availability which resulted in large non-routine flaring and Miscible Gas Injection (MGI) process which requires high power consumption. This paper describes an effective integrated approach undertaken through flaring reduction initiatives, operational excellence and new technology deployments to reduce GHGE footprint within the company/Oman. These initiatives contribute towards a greener hydrocarbon industry and helps in combating climate change.

Non-routine Flaring has been reduced through initiatives to improve system availability (upgrade of control and safeguarding systems, critical parts), focused maintenance campaigns and inline well testing. Critical compressors are being spared where economical and upcoming projects are adopting parallel configurations. Flare Gas Recovery solutions are being deployed with help of new technologies (gas sweetening and flue gas cleaning) to recover all safety flaring and partial non-routine flaring volumes. Local Fuel gas consumption is being phased out. Power grids are being sourced from renewable sources and power plants are being shifted to hydrogen where possible and deployment of CCUS technologies.

As a result of the ongoing initiatives, South Oman Cluster facilities recently in 2021 achieved ~50% reduction in flaring volumes and overall ~25% drop in GHGE compared to 2019 levels. In line with the decarbonization roadmap, total GHGE is predicted to further reduce and reach ~0.7 million tons CO2e (67% lower) by 2030 and ~0.3 million tons CO2e (85% lower) by 2044. These radical impacts were a result of an integrated approach to GHGE issues by identifying all possible sources of emissions, mitigating them as much as possible and addressing the rest through various pragmatic solutions despite the challenges of being critical sour facilities. A core GHGE task force was put in place to continuously evaluate performance, scout for feasible initiatives and pursue its execution to realize its gains. South Oman Sour Cluster GHGE journey, learnings and approach could be replicated elsewhere in the Hydrocarbon industry thereby contributing to a Green Planet.
Cloud-Based Machine Learning System for Large Scale Implementation of Virtual Meters for Production Optimization
Authors: Chen Yujun Johnny , Gulati Nikhil , Annamalai Subbu , Delgoshaie Amir , Jin Zhaoyang Larry , Muradov Riyad
Published: October 2022

This paper proposes a virtual metering (VM) system using machine learning and well production data for estimation of emulsion, produced gas, and vapors for steam-assisted gravity drainage (SAGD) production system. The system consists of four types of virtual meter models for every well: Emulsion Rate VM, Produced Gas Rate VM, Steam Vapor Rate VM, and Flash Vapor Rate VM. Emulsion Rate VM is used to estimate the oil rate output, while the other three VM types are used as the risk level indicators. For optimal well production and well management, operations need to consider both oil production and risks at the same time. Providing both well rate estimation and ability to assess risk at near-real time is a challenging task for large production fields, especially when the data is sparse and there is uncertainty in the well data. We propose a comprehensive system that utilizes a scalable framework to develop and deploy data-driven but process-informed virtual meters at scale and at near-real time. Specifically, in this paper, we describe a) a robust well data-preprocessing pipeline developed to continuously process the stream of input and target data for online learning. b) a state-of-the-art modeling framework designed to combine and segment large amounts of training data from the production filed. c) a scalable and flexible framework to ensure stable model performance with low data latency. d) a streamlined domain-specific model monitoring and MLOps process to enable tuning and retraining of the models by monitoring the performance and data drift.
Not All Unconventional Reservoirs are Similar MENA Regional Vs Global Anisotropic Rock Index and Mechanical Characterization – Part 2
Authors: Gramajo Eduardo , Rached Rached M
Published: October 2022

Improved understanding of unconventional formations requires advanced mechanical and index assessments to explore their complex geology, fissility, and anisotropic behavior. This publication is an extension of the work presented in (Gramajo and Rached 2022), which presented comprehensive datasets of unconventional rocks from China, the United States of America (USA), Canada, and Saudi Arabia. The datasets include the mineral composition, petrophysical parameters (Total Organic Carbon (TOC), porosity, and permeability), and mechanical properties (elastic parameters and compressive strength values).

This paper extends the analysis to include unconventional formations from the Middle East and North Africa (MENA) datasets, specifically from Bahrain and the United Arab Emirates (UAE).

The study enhances our understanding of the newly added rock formations and defines the rock analogs and initial parameters needed to tailor down-hole tools, fracturing fluids, and engineering processes. The results will help reduce the costs (equipment, sample preparation, and measurement time) associated with the initial experimental assessments and achieve higher production outcomes in the emerging reservoirs.
NEB De-Manning and Digital transformation
Authors: Banihammad Basma Ali , Al Zaabi Abdulla Rashed , Al Kindi Hamoud , Modi Jigar , Rafat Mohannad
Published: October 2022

With objectives of the greatest importance such as people safety, operational excellence, collaborative culture and improved system integrity in mind, ADNOC Onshore has evaluated several business processes and explored possibilities with multiple technology providers. Post this evaluation, ADNOC Onshore initiated the NEB Field (North East Bab) De-manned Operations Initiative, which is considered one of the main pioneer projects patronaged by ADNOC Upstream. The project has achieved anticipated manpower reduction and other planned objectives which in turn has generated direct and indirect benefits. The future technology projects with the same intention will have the strong platform provided by the NEB De-Manning project and continue to build on the success.
Novel Frac Sleeve Technology Designed to Replace Plug N Perf and Improve Efficiencies While Significantly Reducing Time to Production and Environmental Impact
Authors: Gibson Chad Michael Erick
Published: October 2022

The paper aims to describe the design, testing/validation, applications, and field trial results of a 15K Multi-Stage Fracturing (MSF) Intervention-less sleeve system for use in single-point entry or multi-cluster design. Field proven results show efficiencies over plug n perf, coil annular frac and ball drop operations and significant environmental savings.

Today, pad design can depend highly on how fracturing operations will be performed. Time between stages, water usage and services required can determine the optimal design of choice when choosing between a single well or multi well pad. Implementing an intervention-less sleeve system reduces the time and water usage between stages and eliminates the need for zipper operations to achieve surface efficiencies. These systems provide completion efficiencies regardless of the number of wells on a pad. By identifying the critical operational limitations of legacy MSF technologies, this paper demonstrates clear benefits between the new intervention-less system and legacy multistage fracturing tools and methods.

Performance of current dissolvable solutions is dependent on wellbore conditions and highly variable from one well to the next. Using material science and product design, the time to production is significantly reduced by eliminating the variability experienced when using the current dissolvable products.

An operating efficiency and downhole frac performance comparison between the intervention-less system and Plug n Perf applications in single well operations is provided. The elimination of interventions, wireline, pump down, explosives, and drill out prove to provide significant benefits over current completions methods and reduced; cost, time, and environmental impact.
Remote Operations of Oil & Gas Fields – A Key Enabler to Achieve Operational Excellence and Digital Transformation
Authors: Banihammad Basma Ali , Al Zaabi Abdulla Rashed , AlAhbabi Nasser Hassan , Al Kindi Hamoud , Al Ameeri Abdalla Qambar , Abdul Zahir Usama , Alhammadi Sara
Published: October 2022

ADNOC is pursuing Digital Transformation by transforming the operating model of assets from manned operations to de-manned operations. The vision is to establish a Centralized Digital Control Centre capable of fully controlling onshore and offshore assets remotely; hence optimize the operational efficiency.

NEB ADNOC Onshore is pursuing an ambitious Digital Transformation plan aligned with ADNOC 2030 strategy. The strategy mandates to deliver a Digital Control Centre capable of fully controlling the NEB asset remotely.

As a POC, an in-house initiative was materialized to leverage the capabilities of installed advanced technologies to replicate Rumaitha Control Room in AlNouf while providing all infrastructure required to fully operate RA facility remotely, without compromising the safety and integrity of facilities.
Data Analytics and Machine Learning Predictive Modeling for Unconventional Reservoir Performance Utilizing Geoengineering and Completion Data: Sweet Spot Identification and Completion Design Optimization
Authors: Leem Junghun , Mazeli Abd Hakim , Musa Ikhwanul Hafizi , Che Yusoff M Fakharuddin
Published: October 2022

Performance of wells in an unconventional reservoir are largely diverse due to different geology, reservoir characteristics, and completion design. A comprehensive method of data analytics and predictive Machine Learning (ML) modeling was proposed to identify production zone "sweet spots", and optimize completion designs from reservoir quality (e.g., geological, geophysical, and geomechanical) data and completion quality data (e.g., frac stage spacing, fluid volume, and proppant intensity, in order to enhance performance of production wells in unconventional reservoirs.

Typical data analytics and predictive ML modeling approach utilizes all the reservoir quality data and completion quality data together, which mostly leads to domination of the completion quality data over the reservoir quality data because of higher statistical correlation (i.e., weight) of the completion data to observed production. Hence, resulting predictive ML models commonly underestimate the effects of the reservoir quality on production, and exaggerate the influence of the completion quality data. To overcome the shortcomings, the reservoir quality data and the completion quality data are separated and normalized independently. The normalized reservoir and completion quality data are utilized to identify sweet spots and optimize completion design respectively, through predictive ML modellings.

The patent-pending methodology of predictive ML modeling has been exercised in recently developed wells of the Montney unconventional shale gas formation, British Columbia Canada, and identified sweet spots from key controlling reservoir quality data and as well as prescribed optimal completion designs from key controlling completion quality data.
Integrated Reservoir, Geology and Geomechanical Characterization for Unconventional Development: Application to the Diyab Play
Authors: Pourpak Hamid , Hernandez Eider , Higelin Maxime , Jaber Maysaa , Mansoor Khalid , Sullivan Luther , Baud Emmanuelle , Su Kun , Zeeshan Baig Muhammad , Al Marzooqi Hassan , Van Laer Pierre , Alharthi Amena D. , Al Hashmi Abdulla Ali , Brooks Trevor Daniel , Al Arbai Mohamed Elsayed
Published: October 2022

The Diyab Formation is an organic-rich carbonate rock with low permeabilities and is one of the first unconventional targets to emerge in the Middle East. Vertical and horizontal exploration wells were drilled during the past years with proven productivity in the United Arab Emirates (UAE). Coupled geomechanical and reservoir characterizations of the Diyab formation are crucial for the successfulness of Stimulated Rock Volume (SRV) Creation and hydraulic fracturing operations which can have a direct impact on production performance. The objective of this study was to perform a full characterization of the Diyab formation based on extensive datasets that include logs and cores. The outcome of this integrated characterization work is used to assess the behavior of the Diyab formation across the concession block.

First, we present the geology and general context of the studied area. Next, we detail the current understanding of the structural lineaments and natural fractures across the block. Then, based on full characterization work originating from data acquired on exploration and appraisal wells, we show how the results of geomechanical characterization together with the analysis of reservoirs quality/geological data allow us to suggest a vertical sub-division for Diyab formation. We explain further how the reservoir/geology, geomechanical parameters and natural fractures change laterally between wells.

Reservoir characterization work concluded that there are some lateral variabilities in Diyab formation such as the change in the thickness/mineralogy of the carbonate bench and thickness of the porous wackestone. Some lateral variations in geomechanical/SRV parameters are observed within the block, resulted mainly from change in natural fractures density and properties of the carbonate bench and porous wackestone. This work is the first result of the integration of the current available data and the knowledge on Diyab formation, which could potentially evolve with the acquisition of new data and analyses.

The combination of a full geomechanical characterization with a reservoir quality and structural geology study allows to propose a detailed reservoir and geomechanical sub-division for the Diyab formation. This approach will aid to better understand the lateral variability of facies, reservoir quality and geomechanical properties within the block which are crucial for successful development of this unconventional play.
A Novel Multiphase Workflow for Tight Unconventionals, with Eagle Ford Well Example
Authors: Majzoub Dahouk Mohamad , Lia Carlsen Mathias , Yusra Ilina , Hays Whitson Curtis
Published: October 2022

The goal of unconventional reservoir characterization is to forecast production based on historical well performance. This has been historically achieved using three main categories of modeling (1) decline curve analysis (DCA), (2) rate transient analysis (RTA), and (3) finite-difference numerical modeling. In the past 4 years, new technologies have emerged that, when used together, allow maximum consistency in production forecasting between these modeling approaches. The objective of this paper is to present a novel multiphase workflow integrating the most recent developments in unconventional reservoir characterization, and applying the workflow to an Eagle Ford well example provided in the SPE Data Repository.

The unconventional reservoir characterization workflow presented in this paper can be divided into 6 main steps. (1) PVT to correctly describe the fluid behavior and fluid properties using flowback data (Younus et al. 2019, Carlsen et al. 2020); (2) bottomhole pressures, to measure or estimate the flowing bottomhole pressure of each well; (3) multiphase flowing material balance, to estimate the contacted pore volume and contacted OOIP / OGIP (Thompson & Ruddick 2022); (4) numerical RTA, to quantify early time well performance using the linear flow parameter (LFP) (Bowie & Ewert 2020, Carlsen et al. 2021); (5) numerical modeling & forecasting; to estimate the EUR with different bottomhole pressure forecasts; and (6) decline curve analysis; to input DCA parameters into economic evaluation workflows. Common challenges and best practices are provided for each of the 6 steps of the workflow, resulting in a comprehensive workflow that can be readily applied consistently by any reservoir engineer working with unconventional reservoirs.

The proposed workflow has been used on many hundreds of wells covering all the ultra-tight unconventional / shale basins in North America. In this paper, the workflow is specifically applied to a well in the Eagle Ford, yielding (but not limited to) initial fluids in place, reservoir rock properties, PVT, well completion factors, fracture area and conductivity, and definition of infinite-acting and boundary-dominated flow regimes. The most relevant characteristics of the proposed workflow are (1) it can be automated and would require less than 30 minutes of engineering analysis per well; (2) it results in a full physics numerical model (finite difference) that can readily be used for any further studies. The proposed workflow is rigorous, complete, and practical, the first of its kind – (1) rigorous, leveraging existing and proven technologies, (2) complete, including every key engineeringstep from the PVT to production forecast and (3) practical, being used currently for more than a thousand wells in several North American basins.
Digital Transformation Drives Reduction of the Inactive Strings Count. A Success Story in a Major National Oil Company.
Authors: Al Braiki Warda Awadh , Davila Rayner Samuel , Molina Adriana Del Carmen , Azhar Mohammed , Shaik Zayed Iqbal , Alyaqoubi Afra Saeed , Eldin Haitham Alaa
Published: October 2022

This paper demonstrates a successful case story of how harnessing digitalization has significantly improved the Management of Inactive wells. An integrated web-based application was developed in house and fully deployed across the Company. It has demonstrated an immense added value by bringing key stakeholders in an automated and collaborative environment to standardize and improve the existing well reactivation cycle and mitigating well & facilities integrity threats.

Inactive Strings Management System (ISMS) is tailored made for multiple disciplines such as production, petroleum, well integrity, facilities and reservoir engineers, as well as Corporate Units and Management, all connected through standards automated workflows.

The new solution is composed by 13 main modules that covers the whole well reactivation cycle: a) Data Entry b)Validation, c) Endorsement, d) Live Well Inventory, e) Tracking and Monitoring, f) Well Problem & Status, g) Dashboards, h) Well history, i) Well Prioritization, j) Manager Approval, and k) Level 2 KPI's. 1) Managing Problematics Wells, and m) Project Related.

The Inactive String Management Tool has proved to be vastly beneficial. Since its implementation in December 2021, it has successfully supported the reactivation of around 810 strings, and monitoring over 4800 operating strings, with an associated cumulative oil rate gain of 490,000 barrels, translated into a profit of around $23 MM (USD), and shortened the well reactivation cycle by 20%.

The solution had led to major improvements on the current business and system processes by addressing major challenges including data integrity issues, lack of automation, systems integration, poor tracking of well reactivation actions, and most importantly, absence of a systematic approach to prompt multidisciplinary well revision by having a unique planform to manage the inactive strings, thru standardization of best practices and lessons learned across Assets.

It has increased the well problem visibility and accountability of key stakeholders to firm, maturate and accelerate well reactivation actions in an automated and collaborative environment, reducing silos, ensuring alignment, sharing knowledge and information more effectively. Improved resources planning and production gain without jeopardizing well and facilities integrity.

This solution is part of the Oil and Gas 4.0 company digital transformation strategy, encourages collaboration across departments, fostering a digital culture to rapidly react to business changes. It has laid down the foundation to sustain growth and a competitive edge in the digital age by applying deeper and more targeted analytics that enable better business decision-making, such as predictive analytics, artificial intelligence, and business process management.
Digital Control Tower – Instantaneous Visibility, Granularity and Decision Support for an LNG Mega Project
Authors: Srivastava Yogesh Chandra , Srivastava Abhishek , Granata Consuelo , Garg Tanvi
Published: October 2022

Objectives/Scope

Cloud based end to end digital project management platform with digital control tower, as fundamental technologies of the fourth industrial revolution, have enormous potential to improve real time visibility, productivity, collaboration, data sharing, efficiency, predictability, decision power and sustainability in the construction industry.

Throughout the lifecycle of capital / mega projects, the engineering, construction, operations, and maintenance stakeholders face numerous issues, including the lack of trust and visibility, inefficiencies, and the fragmentation of the information value chain into fragile data silos. Digital control tower aimed to narrow down the time, budget and quality & safety challenges of a capital project by having overall / end to end control of project from initiation phase to handover and operations phase of the project.

Methods, Procedures, Process

Digital control tower provides systematic, 360-degree view of project details along with real-time analytics spanning from design engineering, pre-construction planning, onsite execution, action tracker, construction services, equipment's, quality, budget and safety (leading and lagging indicators).

Stakeholders involved throughout the project lifecycle (i.e.: owner, engineering team, pre-construction and planning team, construction management and site team), enables instantaneous connectivity from the top floor to the shop floor for seamless communication by reducing waste time.

Digital control tower also enables bird's eye view for real time project performance monitoring and progress, it also enables week wise look ahead task for better monitoring and control, also narrows down the issues or concerns to an activity, area or person. The control tower can create the customizable necessary discipline/function dashboards available to all relevant roles and stakeholders without limitation.

Results, Observations, Conclusions

Digital Control Tower leverages Artificial intelligence, Machine learning and Blockchain technologies to seamlessly capture, maintain and process fragmented data set into intelligent predictive analytics which helps the project stakeholders to take right decision on right time, so as to avoid any kind of delays in project.

Novel/Additive Information

Digital Control Tower is not a standard dashboard, it's an integrated digital ecosystem where stakeholders can drive projects with real time data for decision making.
Deep-Learning-Based Surrogate Reservoir Model for History-Matching Optimization
Authors: Maarouf Alaa , Tahir Sofiane , Su Shi , Ramatullayev Samat , Rat Coriolan , Kada Kloucha Chakib , Mustapha Hussein
Published: October 2022

Achieving a high-quality history match is critical to understand reservoir uncertainties and perform reliable field-development planning. Classical approaches require large uncertainty studies to be conducted with reservoir-simulation models, and optimization techniques would be applied to reach a configuration where a minimum error is achieved for the history match. Such techniques are computationally heavy, because all reservoir simulations are run in both uncertainty studies and optimization processes.

To reduce the computing requirements during the optimization process, we propose to create a robust deep-learning model based on the hidden relationships between the uncertainty parameters and the reservoir-simulation results that can operate as a surrogate model for computationally intensive reservoir-simulation models.

In this paper, we present a workflow that combines a deep-learning, machine-learning (ML) model with an optimizer to automate the history-matching process. Initially, the reservoir simulator is run to generate an ensemble of realizations to provide a comprehensive set of data relating the history-matching uncertainty parameters and the associated reservoir-simulation results. This data is used to train a deep-learning model to predict reservoir-simulation results for all wells and relevant properties for history matching from a set of the selected history-matching uncertainty parameters. This deep-learning model is used as a proxy to replace the reservoir-simulation model and to reduce the computational overhead caused by running the reservoir simulator. The optimization solution embeds the trained ML model and aims to deliver a set of uncertainty parameters that minimizes the mismatch between simulation results and historical data. At each optimization iteration, the ML model is used to predict the well-level reservoir-simulation results.

At the end of the optimization process, the optimal parameters suggested by the optimizer are then validated by running the reservoir simulator.

The proposed work achieves high-quality results by leveraging advanced artificial-intelligence techniques, thus automating and significantly accelerating the history-matching process.

The use of uncertainty parameters as input to the deep-learning model, and the model's ability to predict production/injection/pressure profiles for all wells is a unique methodology. Furthermore, the combination of the deep-learning surrogate reservoir model with optimization methods to resolve history-matching problems is advancing the industry's practices on the subject.