bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementing ASP Without Water Softening in Reservoirs With Low Concentrations of Divalent Ions
Authors: Farsi Samya , Bouwmeester Ron , Brewer Mark , Southwick Jeff , Mahruqi Dawood , Karpan Volodimir , van Batenburg Diederik , Carpenter Ian , Wilson Sam 
Published: March 2022

Abstract
Alkaline Surfactant Polymer (ASP) flooding has proven to be an effective method to recover residual oil after waterflood. Yet, there are several complications that limit a wide implementation of ASP. The source water needs to be softened to avoid injectivity issues due to scale formation when alkali is mixed with divalent cations. Even when softened water is used as base water, scaling can still be an issue in producing wells due to mixing of waters with different compositions in-situ. Scale control by means of scale inhibitor squeezes on the production side or scale inhibitor on the injection side have been reported to be successful for some cases.
Carbonate scaling issues are most severe when sodium carbonate is used as alkali. Even if alkalis other than carbonate are used, carbonate scale remains an issue as bicarbonate that is present in almost any formation water, will convert to carbonate at high pH and will subsequently precipitate with the divalent ions present.
Ethanolamine has been proposed as an effective alkali in ASP application with high TAN oils for reservoirs that contain relatively low concentrations of divalent ions in their produced water that is reinjected. ASP implementation could significantly be simplified if softening of the produced water could be avoided.
Results of a study that involved determination of scaling tendencies, compatibility testing, tube blocking tests, and core floods with and without the addition of scale inhibitor will be presented together with implications for field implementation. A potential additional advantage of adding scale inhibitor on the injection side is that the scale inhibitor travels with the components to be inhibited. This might eliminate the need for scale control measures at the production side.
In-Situ Stresses and Geomechanical Properties Characterization Using Robust Optimization Approach, Tested in a Tight Gas field, Sultanate of Oman
Authors: Saidi Hamdan , A-Aamri Mohammed , Al - Senani Ahmed , AL-Aani Khalid 
Published: March 2022

Abstract
Knowledge of in-situ stresses and geomechanical properties is important for wellbore stability and hydraulic fracture optimization applications. Both mechanical rock properties (e.g., Young's modulus and Poisson's ratio) and the stresses represent the initial step in constructing a geomechanical model that will eventually require static calibration from the lab or field tests. Nonetheless, a wellbore deformation-based inverse analysis solution has become an alternative method that characterizes in-situ stress in particular. In this paper, a genetic algorithm and probabilistic analysis methods are proposed and integrated into a well-drilled known analytical method to characterize both stresses and geomechanical properties.
Systematic steps have been applied to this analysis. First, borehole geometry (i.e., multi-arm caliper), mud weight, and vertical pressure (from the density log) are well-defined inputs for deformation-stress relationships. Unknown parameters have also been determined and include horizontal stress, Poisson's ratio, and Young's modulus. Subsequently, the minimum and maximum expected values for each unknown parameter have been defined. Thousands of combinations have been created by the analytical equation (fitness function). In addition, the semi-genetic algorithm concept was used as an optimization method to find the best solution from a wide range of inputs for a given fitness function. The first hundred strongest fitness combinations were then chosen for the next level, which had a noticeably higher frequency number using the statistical analysis technique. The approach was checked with a real field example, the results indicated the measured values of geomechanical properties, and horizontal stress were reasonably consistent with the actual field data and previous studies in the field. In particular, the proposed approach allows for a realistic estimate of the most difficult stress (i.e., Max horizontal stress), which was ~45 % higher than minimum horizontal stress.
The proposed technique was developed to reduce in-situ pressure uncertainties and geomechanical properties for the studied area. Results from this paper presented a simple and practical alternative method for the determination of geomechanical parameters using a simple logging tool (e.g., a caliper) that theoretically provides a robustness guide for wellbore stability and hydraulic fracture models for tight gas fields.
Evergreen Forecast & Predictive LTRO Using Machine Learning – Case Study from PDO South
Authors: Mahaldar Sahil , Singh Jasbindra , Riyami Abdullah , Mahrooqi Nasser , Abri Mohammed , Mandhari Sulaiman , Hikmani Salwa , Al Humaid Maitham , Sinani Yousuf , Mahruqi Issa , Al Azri Nasser , Mohajeri Sina 
Published: March 2022

Abstract
Digital transformation (Dx) is increasingly becoming a key enabler in oil and gas industry to reduce costs, make faster and better decisions and increase productivity. The difference between leading the next innovation wave or being left behind may depend on how proficiently we embrace digital enablers, and how quickly we can test, prototype and scale these digital solutions to create value for the business.
Digital technologies are not new to Petroleum Development Oman (PDO). In fact, the company has a track record of testing and adopting a wide range of new technology and integrated organisational capabilities to improve its business performance. Significant investments have been made into instrumenting its fields, including the IT infrastructure, Real-Time Operations, Exception Based Surveillance, Collaborative Work Environment (CWE), Smart Fields, NIBRAS, data management, analytics trials, to name a few. Yet consensus that Dx has significant further upside across PDO, led to the initiation of an asset-led pilot to digitally transform an existing PDO South Field – "S". The focus of the pilot was to identify new Dx opportunities while leveraging on existing PDO investments into digitalization, leading to quantified improvement in business performance of field –S. The project workscope was based on the outcome of an Opportunity Framing Event (OFE), in which a total of 27 opportunities were identified and ranked in terms of business value vs. feasibility or cost of implementation (Figure 1). Technical Subject Matter Experts (SMEs), asset field - surface, sub-surface, data management teams and other relevant support functions participated in the OFE so that business improvement synergies could be identified across the multiple disciplines in an integrated fashion.
Following an agile approach, 5 Valuestreams (VS) were selected for Minimum Viable Product (MVP) implementation, in phase 1 of the pilot. Focus of this paper, however, is to elaborate further only one of the 5 VSs i.e. use of machine learning for "Evergreen Production Forecast for Field Development Plan (FDP) optimization and Locate the Remaining Oil (LTRO)".
An Improved HLD-NAC Model for Microemulsion Phase Behavior Study
Authors: Bu Xingang , Han Ming , AlSofi Abdulkareem , Fuseni Alhasan 
Published: March 2022

Abstract
Microemulsion phase behavior studies are essential for screening surfactants for improving oil production. The paper presents a modified Hydrophilic-Lipophilic Deviation – Net Average Curvature (HLD-NAC) model to explicitly express the solubilization ratio with respect to a newly defined inherent parameter representing surfactant properties. It also provides a workflow to extract the critical parameters from experimental results for numerical simulation of a chemical flooding process. The equations were derived to calculate the window of Winsor Type III microemulsion. The HLD-NAC model was improved to make the solubilization ratio explicitly expressed with the inherent parameter composed of head area (as), tail length (L) and molecular weight of the surfactants (Mw). An innovated workflow was developed to integrate the microemulsion phase behavior scanning results with the HLD-NAC model. The HLD-NAC model was validated with experimental data of various surfactant formulations.
The inherent parameter was applied in the improved HLD-NAC model, making it efficient for modeling microemulsion phase behavior scanning. The data of solubilization ratio and phase volume fraction were well fit in the model without needing to know the details of surfactant properties. This simplified the input of HLD-NAC parameters and made the output more accurate. The HLD-NAC model was successfully validated by experiments with various formulations including surfactant with alcohol, surfactant mixtures, and sodium carbonate-surfactant mixtures. By automatically obtaining the inherent parameter, the improved HLD-NAC model provides a promising application of microemulsion phase behavior in numerical simulation of chemical flooding. The HLD-NAC model is modified by defining an inherent parameter from experimental results of microemulsion phase behavior scanning. This makes it applicable to generate the solubilization ratio parameter for numerical simulation of chemical flooding.
The Role of Polymer on Surfactant-Foam Stability During Carbon Dioxide Mobility Control Process
Authors: AlYousef Zuhair , Swaie Othman , Alabdulwahab Amin , Kokal Sunil 
Published: March 2022

Abstract
The in-situ generation of foam is one of the most promising techniques to solve gas mobility challenges in petroleum reservoirs and subsequently improve the volumetric sweep efficiency. The stabilization of foam at reservoir conditions is a major challenge. The harsh reservoir conditions, such as high temperature, high brine salinity, together with surfactant adsorption on the rock may result in unstable foam and, consequently, poor sweep efficiency. Foam additives, such as polymers, might help strengthen the physical properties of foam film and improve foam stability. This work evaluates the effectiveness of a polymer on enhancing CO2-foam stabilization at harsh reservoir conditions.
Static and dynamic foam tests were conducted to evaluate the role of polymer on foam stability. Three foaming surfactants were used to assess the ability of the polymer on enhancing foam stabilization. The static foam tests were conducted at conditions similar to reservoir conditions using test tubes. Foam column, and foam life were measured to evaluate the role of the polymer on foam stabilization. Foam viscosity in absence and presence of the polymer was measured using foam rheometer apparatus. The dynamic foam tests were conducted to assess the ability of tested materials to generate viscous foams and also measure the CO2 mobility in porous media using a coreflooding system. The mobility reduction factor (MRF) was measured at high pressure and high temperature (HPHT) conditions, 3200 psi and 100°C.
The static foam tests and foam rheology measurements indicated that the addition of the polymer enhanced foam stability as a result of increasing the bulk viscosity of the aqueous solutions. The results found that the foam life increased with the polymer concentration. However, the increase of polymer concentration makes the solution very viscous, hence, the foam generation becomes challenging. The dynamic foam tests showed that the foam generated in absence of the polymer was able to reduce the CO2 mobility 13 fold. However, the addition of the polymer resulted in higher pressure drops during CO2 floods, more resistance to gas flow and, therefore, lower gas mobility compared to that obtained with surfactant alone. The addition of the polymer reduced the CO2 mobility 50 fold. This higher reduction in the CO2 mobility as a result of adding the polymer can be attributed to the effectiveness of the polymer in improving the foam stabilization and prolong the life of generated foam.
Conventional Tight Gas Field Through Unconventional Eyes: Data Analytics Help to Optimize Fracture Design and Operations
Authors: Yuan Roger , Bahri Khalfan , Veeken Cornelis , Shoaibi Sultan 
Published: March 2022

Abstract
Deep tight gas fields in Northern Oman are often compared to and approached with unconventionals due to their tight matrix properties and the necessity of employing hydraulic fracturing to deliver productivity. Complicated by operational constraints and field histories, hydraulic fracture effectiveness – how fracture stimulation delivers relative to how much matrix flow contributes to production – remains a puzzle and a challenge. This further affects how to optimize existing completion and stimulation strategy in order to improve the value proposition.
In this study, we review the fracture and production performance of a mature gas field in Northern Oman. Integrating data of various technical disciplines, we re-examine a wealth of cumulative field data over two decades of operations with an aim to identify the key enablers for fracture placement and production. With integration of reservoir properties, geomechanics, and time-lapse production profiles, we identify that geomechanics plays a key role in controlling reservoir fraccability and the placement of hydraulic fractures. While hydraulic fracture containment within the Barik formation has been well recognized and considered a given in multi-staged fractured vertical wells, the creation of fracture heights is found dependent on the in-situ stress conditions and pumping metrics, which further links to productivity. Such inter-relationship could potentially be utilized to optimize fracture performance by a refined placement strategy.
With big data, the common technical opinions that normally arise from a deterministic approach on limited data can be better visualized and addressed. The statistical strength of the analysis leads to improved understanding of the subsurface complexity, interaction of reservoir quality with completion design, and a suite of future optimization opportunities.
Optimising Waterflood Performance in Mature Fields, Using Autonomous Inflow Control Valve Technology
Authors: Shihab Sufyan , Khadhuri Shaikhan , Saadi Hamoud , Aakre Haavard , Mathiesen Vidar 
Published: March 2022

Abstract
Managing increasing volumes of produced water is one of the main challenges faced in waterflood fields. Better water mobility -compared to oil-, results in increasing water cut (WC) and reduced oil production, with time. In Qarn Alam, with mature brown fields depleted through waterflooding, oil producers are completed as open-hole horizontal wells. This means well experience water breakthrough from day one, due to poor oil-to-water mobility ratio, toe-heel effect, permeability variations and matrix fractures. Inflow Control Devices (ICD) has demonstrated good efficiency in delaying water breakthrough, however, when breakthrough is a given factor, ICDs lose that efficiency. The Autonomous Inflow Control Valve (AICV®) is a novel powerful tool that helps reducing WC in favour of oil, based on contrasts in fluid viscosities. The tool has a movable piston that opens/closes autonomously -without any connection to surface-, to reduce the unwanted water production, where choking increases with increasing WC. AICV technology was deployed in several oil producers, in carbonate reservoir brown field. The field has produced for decades, and is facing an increasing water management challenge. AICV completions were designed such that wells are divided into compartments isolated by swellable packers. The comparison of historical production data without and with autonomous inflow shows clearly the benefit of installing autonomous inflow control. Preliminary assessment showed an average 75% reduction in water-cut and 20% average increase in oil rate.
What is the Best Artificial Intelligent Technology to Solve Drilling Challenges?
Authors: Gharbi Salem , Al Majed Abdul Azeez , Abdulraheem Abdulazeez , Patil Shirish , Elkatatny Salaheldin 
Published: March 2022

Abstract
Drilling operation started drilling more challenging wells that is farer, deeper and in unconventional conditions; this require the drilling industry to adopt new technologies supporting them in these challenges. One of the big potential supporting technologies is the artificial intelligent. Artificial intelligent could help drilling engineers and operation crew in crunching the massive amount of drilling data converting them to decision-like format, leading to safer, faster and more cost effective operations. The challenge is that artificial intelligent projects consists of multi dimension tasks, starting from data handling, infrastructure building, through model development and integrating with existing environment. Such tasks confuses even IT teams, so developing artificial intelligent projects targeting drilling domain will be very tough. Experience shows that a lot of oil & gas artificial intelligent projects fails due to miscommunication between the business domain experts and the artificial intelligent, not having common understanding, problem in the data, or model computability issues could be also other reason for such failure.
This paper propose a methodology that will increase the possibility of having success artificial intelligent drilling project. This methodology is CRISP-DM which stand for Cross Industry Standard Process for Data Mining. This methodology consist of the following phases Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. This paper is shading lights on these phases, also it will derive the readers through a drilling case-study, where this methodology was applied leading to successful artificial intelligent drilling project.
Evaluation of Inflow Control Device Effectiveness to Mitigate Thermally Induced Fractures in Injection Wells
Authors: Almarri Misfer 
Published: March 2022

Abstract
Injection of cold fluid is injected into hot reservoirs and rocks undergo contraction due to temperature difference. This contrast in temperatures causes the in-situ stress to reduce considerably. When the Minimum Horizontal Stress (σhmin) falls below the Bottomhole Pressure (BHP) due to temperature changes, fractures may initiate and/or propagate. Fractures resulted from thermal processes is referred as Thermally Induced Fractures (TIFs). TIFs can cause highly non-uniform distribution of the injected water flow in the wellbores, reduction in the sweep efficiency, and early water breakthrough in the nearby production wells. The objective of this paper is to evaluate the effectiveness of Inflow Control Device (ICD) to mitigate these fractures in water injection wells.
A real field history matched sector model with evidence of TIF occurrence is utilized in this paper using a 3D reservoir thermal simulator coupled with a 2D TIF model and a geomechanical model. The impact of different completions in injection well with TIF modelling under different scenarios is investigated.
The added value of ICD was quantified and proved to be effective in controlling TIF initiation and propagation as well as in improving the wellbore flow performance. The selected ICD size should be neither too big (no control) nor too small (over-restriction of injection rate).
TIFs mitigation method proposed in this paper is practical, efficient, and strongly contribute to the research aimed at improving waterflood performance in oil fields. Recommendations and guidelines can be utilized in waterflooding operations during modelling, designing, and planning stages.
Condensate Optimization Through Digital Tools
Authors: Al Farsi Ghaida , Jayawickramarajah Angeni 
Published: March 2022

Abstract
Khazzan, the gas giant in Oman, and its new gas processing facility were started up in 2017 delivering a daily contractual volume of gas o the local grid. The wells had high gas deliverability potential, and yet they were constrained to deliver the contractual volume specified by the commercial agreement with the government. To maximize the value of this excess gas potential, a new digital tool was utilized to optimize our condensate production – increasing production by 2-3% with no additional cost.
The first step was to create a digital twin of the asset, including the structure of the asset and the fluid dynamics of the flow regimes, temperatures and pressures. Historical data was used to populate the digital twin, and sensors in the asset were set up to send real time data to the functioning twin of the physical asset. Any constraints in the production system were built into the digital twin to provide the most accurate simulation. The tool was then used to monitor, simulate and optimize production, by testing multiple variables until an optimal solution was found for the entire production system from wells, through facilities, to export.
Significant value was obtained by utilizing the digital toolkit, delivering not only economic value but progressing innovation as well. Traditional production management requires considerable time to manually integrate the complex infrastructure and fluid dynamics. This digital twin of Khazzan enabled the work to be automated and completed much faster than conventional methods–allowing petroleum engineers to focus on evaluating well performance rather than running time-consuming scenarios of anticipated or likely events. The response time to any changes in gas nomination was also reduced significantly, by running simulations with a few clicks on the screen and updating them in the live streaming Wells Overview sheet.
Condensate optimization was not only achieved through maximizing condensate production, but there an opportunity was realized to minimize condensate flared volumes during plant upsets which ultimately impact recovered condensate. This was completed in conjunction with the live streaming Wells Overview – to capitalize on utilizing a digital workflow to reduce flaring volumes.
Overall, this digital twin benefited BP Oman by indicating where efficiencies can be improved and potential problems in the production system, leading to significant business value being added to the company.