bluuebunny/crossref_metadata_2025 huggingface dataset:
Fugitive Emission Monitoring System Using Land-Based Sensors for Industrial Applications
Authors: Roy Arjun , Datchanamoorthy Senthilkumar , Nundy Sangeeta , Keely Bhaskerrao , Kim Okja , Chan Godine
Published: December 2021

Metal-oxide based emission detection sensors are typically used for point measurements of hydrocarbon emissions. They are low-cost sensors and can be used for continuous monitoring of emissions. This paper describes an analytical framework that uses time series data from a collection of such sensors deployed at a customer site, along with weather conditions, to detect anomalies in emission data, identify possible emission sources and estimate the leak rate from fugitive emissions. The analytical framework also comprises an optimization module that helps in determining the optimal number of sensors required and their potential location at a customer site. The paper discusses results of the different steps in the analytical framework obtained using concentration data generated using numerical simulations and obtained through controlled leak field tests.
Lead Application to Cure Sap Wells by Deploying Straddle Packer, Success Story
Authors: Gadelhak Abdelrahman Mohamed , Al-Badi Mohamed , Al-Bairaq Ahmed , Al Mheiri Eissa , Al-Hosani Abdullah Haj , Ahmed Zeeshan , Ahmed Sami Ullah Bashir , Ahmed Mubashir , Abedelkhalik Waleed Omar , Naser Sameh Hassan , El-Shater Haysam , Al Assar Wessam , Ross Steve , Duncan Blair
Published: December 2021

Objective/Scope

The Increase of inactive wells due to subsurface integrity issue is observed in brown fields, Fig-1 is, showing the record for onshore UAE asset, the economic challenges is calling for alternative solutions to restore well integrity with lower cost.

Straddle packer application is consists of two tandom packers with spacer pipe in between with anchoring system deployed riglessly in the well to isolate the communication point between Ann A and Tubing.Fig-2,

Methods, Procedures, Process

Communication between tubing and annulus A (Failure of primary barrier) is identified as the right candidate wells for straddle packer application,

First step is to clearly identify the point of communication, it has been done by annulus pressure investigation excersize during flowing and shut in condition, observing the return of annulus fluid which was the same produced gas

Noise log has been conducted and clearly identified the communication point at SPM (Side Pocket Mandrel) to be used for emergency killing,

Tubing integrity test was conducted using nippless plugs and inflow test below and above the leak point and confirm no other leak points within the tubing

Engineering drawing for the leaking assembly was reviewed to design the dimension of straddle packer assembly, length and packer size

It is recommended to deploy the assembly using electric line correlation for accurate depth selection

After setting annulus pressure observed no build up

Well opened safely to production

Results/Observation/Conclusion

Leak point arrested, well primary barrier restored

Removed from DWS (drilling and workover schedule) and restore well production in addition to improving inactive string KPI for Gas asset

Save almost work over cost for gas well XX-197

Novel/ Additive information

The way forward is to check the scalability of extending this application among other ADNOC assets and to screen the right candidate wells for this application

To add this application as a part of well integrity procedures and recommendations for such like cases
A Tool for Improving Decision Quality on Upstream Consortia
Authors: Cunha J. C. , Bickel J. Eric , Mendoza Luis , Walker Jeremy , Coopersmith Ellen , McKnight Craig A.
Published: December 2021

This article aims to provide a guideline to better decision quality on multi-company upstream projects. The scope is to provide a high-level overview of what should be included in a decision quality process, when companies with different levels of ownership and influence on the decision-making process naturally tend to have different approaches towards risks and decision management.

It is well known in our industry that there is a predominance of multicompany participation in these projects and the paper will provide guidelines that will ultimately provide better decision quality and participant's alignment.

Normally high-risk upstream projects have multi-company ownership. However, it has been noticed that companies tend to face decision management differently, which causes unnecessary delays on budget approvals and even operational timeline. Procedures to normalize the definition of an initial decision frame and creation of solutions based on a good set of alternatives are paramount to facilitate discussions and drive final recommendations.

In summary, companies’ alignment on decision-making processes is key to quality, speed, and effectiveness of those decisions and critical to project success. Alignment between partners on pivotal decisions can significantly improve project delivery.

The main results are practical guidelines for generating (a) decision framing, (b) strategies, (c) alternatives and potential consequences, and (d) logical analysis, partners’ alignment and commitment to action.
Field Implementation of In-Depth Conformance Gel Treatment Prior to Starting an ASP Flooding Pilot
Authors: Al-Murayri Mohammed T. , Hassan Abrahim , Alajmi Naser , Nesbit Jimmy , Thery Bastien , Al Khoury Philippe , Zaitoun Alain , Bouillot Jerome , Salehi Nazanine , Pitts Malcolm J. , Wyatt Kon , Dean Elio S.
Published: December 2021

Mature carbonate reservoirs under waterflood in Kuwait suffer from relatively low oil recovery due to poor volumetric sweep efficiency, both areal, vertically, and microscopically. An Alkaline-Surfactant-Polymer (ASP) pilot using a regular five-spot well pattern is in progress targeting the Sabriyah Mauddud (SAMA) reservoir in pursuit of reserves growth and production sustainability. SAMA suffers from reservoir heterogeneities mainly associated with permeability contrast which may be improved with a conformance treatment to de-risk pre-mature breakthrough of water and chemical EOR agents in preparation for subsequent ASP injection and to improve reservoir contact by the injected fluids. Each of the four injection wells in the SAMA ASP pilot was treated with a chemical conformance improvement formulation. A high viscosity polymer solution (HVPS) of 200 cP was injected prior to a gelant formulation consisting of P300 polymer and X1050 crosslinker. After a shut-in period, wells were then returned to water injection. Injection of high viscosity polymer solution (HVPS) at the four injection wells showed no increase in injection pressure and occurred higher than expected injection rates. Early breakthrough of polymer was observed at SA-0561 production well from three of the four injection wells. No appreciable change in oil cut was observed. HVPS did not improve volumetric sweep efficiency based on the injection and production data. Gel treatment to improve the volumetric conformance of the four injection wells resulted in all the injection wells showing increased of injection pressure from approximately 3000 psi to 3600 psi while injecting at a constant rate of approximately 2,000 bb/day/well. Injection profiles from each of the injection well ILTs showed increased injection into lower-capacity zones and decreased injection into high-capacity zones. Inter-well tracer testing showed delayed tracer breakthrough at the center SA-0561 production well from each of the four injection wells after gel placement. SA-0561 produced average daily produced temperature increased from approximately 40°C to over 50°C. SA-0561 oil cuts increased up to almost 12% from negligible oil sheen prior to gel treatments. Gel treatment improved volumetric sweep efficiency in the SAMA SAP pilot area.
Application of Machine Learning to Interpret Steady State Drainage Relative Permeability Experiments
Authors: Mathew Eric Sonny , Tembely Moussa , AlAmeri Waleed , Al-Shalabi Emad W. , Shaik Abdul Ravoof
Published: December 2021

A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, three different machine learning models was developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. The three different models that were tested and compared were extreme gradient boosting (XGB), deep neural network (DNN) and recurrent neural network (RNN) algorithms. Based on existing mathematical models, a leading edge framework was developed where a large database of Kr and Pc curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were utilized to estimate Kr curves based on Darcy's law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the machine learning model. The entire data set was split into 80% for training and 20% for testing. K-fold cross validation technique was applied to increase the model accuracy by splitting the 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, 9 folds were used for training and the remaining one was used for model validation. Once the model is trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The machine learning model learns to capture fluid flow behavior inside the core from the training dataset. The trained/tested model was thereby employed to estimate Kr curves based on available experimental results.

The performance of the developed model was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective cross plots along with comparisons of ground-truth versus AI predicted curves indicate that the model is capable of making accurate predictions with error percentage between 0.2 and 0.6% on history matching experimental data for all the three tested ML techniques (XGB, DNN, and RNN). This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The results also include a comparison between classical machine learning approaches, shallow and deep neural networks in terms of accuracy in predicting the final Kr curves. The various models discussed in this research work currently focusses on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.
Mooring Sense Project: A Risk-Based Integrity Management Strategy for Mooring System of Floating Offshore Wind
Authors: La Grotta Alessandro , Harris Róisín Louise , Da Costa Clive
Published: December 2021

While Floating Offshore Wind (FOW) represents a significant opportunity to foster wind energy development and to contribute to remarkable CO2 emissions reductions, its associated operational costs are still substantially above grid parity, and significant innovation is needed.

MooringSense is a research and innovation project which explores digitisation technologies to enable the implementation of risk-based integrity management strategies for mooring systems in the FOW sector with the aim to optimise Operations and Maintenance (O&M) activities, reduce costs, and increase energy production.

As part of this project, a risk-based assessment methodology specific for the mooring system of Floating Offshore Wind Turbines (FOWT) has been developed; this allows the development of a risk-based Mooring Integrity Management Strategy that can result in more cost-effective inspection planning. The methodology shall utilise the information made available by numerical tools, sensors, and algorithms developed in the project to update the risk level of the mooring system and set the required plan to mitigate the risk.

Leveraging the additional information from monitoring technologies and predictive capabilities to determine the mooring system condition and remaining lifetime, the strategy provides the criteria for optimal decision making with regards to selection of O&M activities.

The risk-based strategy developed allows for optimal planning of inspection and maintenance activities based on dynamic risk level that is periodically updated through the interface with the Digital Twin (DT).

The validation of the strategy will demonstrate potential cost saving and economic advantages, however, it is expected that the overall MooringSense approach can reduce FOW farm operational costs by 10-15% and increase operational efficiency by means of an Annual Energy Production increase by 2-3%.

The MooringSense project comprises of the development and validation of innovative solutions coming from multiple disciplines such as numerical modelling, simulation, Global Navigation Satellite System (GNSS), Structural Health Monitoring (SHM), and control systems which will provide valuable input to the risk-based mooring integrity management strategy.
Application of a Combination of Machine Learning and Reservoir Physics to Increase Production in a Mature Waterflood Field in the San Jorge Basin in Argentina
Authors: Gomez Javier Eusebio , Robles Marcelo , Di Giuseppe Cristian , Galliano Federico , Centineo Jeronimo , Medda Fernando , Calad Serrano Carlos Mario , Plotno Sebastian , Sarma Pallav , Gutierrez Fernando
Published: December 2021

This paper presents the process and results of the application of Data Physics to optimize production of a mature field in the Gulf of San Jorge Basin in Argentina. Data Physics is a novel technology that blends the reservoir physics (black oil) used in traditional numerical simulation with machine learning and advanced optimization techniques. Data Physics was described in detail in a prior paper (Sarma, et al SPE-185507-MS) as a physics-based modeling approach augmented by machine learning. In essence, historical production and injection data are assimilated using an Ensemble Kalman Filter (EnKF) to infer the petrophysical parameters and create a predictive model of the field. This model is then used with Evolutionary Algorithms (EA) to find the pareto front for multiple optimization objectives like production, injection and NPV. Ultimately, the main objective of Data Physics is to enable Closed Loop Optimization.

The technology was applied on a small section of a very large field in the Gulf of San Jorge comprised of 134 wells including 83 active producers and 27 active water injectors; up to 12 mandrels per well are used to provide with selective injection, while production is carried out in a comingled manner. Production zonal allocation is calculated using an in-house process based on swabbing tests and recovery factors and is used as input to the Data Physics application, while injection allocation is based on tracer logs performed in each injection well twice a year.

This paper describes the modeling and optimization phases as well as the implementation in the field and the results obtained after performing two close loop optimization cycles. The initial model was developed between October and December 2018 and initial field implementation took place between January to March 2019. A second optimization cycle was then executed in January 2020 and results observed for several months.
The Effect of Capillary Condensation on the Geomechanical and Acoustic Properties of Tight Formations: An Experimental Study
Authors: Albannay Aamer , Bui Binh , Katsuki Daisuke
Published: December 2021

Capillary condensation is the condensation of the gas inside nano-pore space at a pressure lower than the bulk dew point pressure as the result of multilayer adsorption due to the high capillary pressure inside the small pore throat of unconventional rocks. The condensation of liquid in nano-pore space of rock changes its mechanical and acoustic properties. Acoustic properties variation due to capillary condensation provides us a tool to monitor phase change in reservoir as a result of nano-confinement as well as mapping the area where phase change occurs as well as characterize pore size distribution. This is particularly important for tight formations where confinement has a strong effect on phase behavior that is challenging to measure experimentally. Theoretical studies have examined the effects of capillary condensation; however, these findings have not been verified experimentally.

The main objective of this study is to experimentally investigate the effect of capillary condensation on the mechanical and acoustic properties of shale samples. The mechanical and acoustic characterization of the samples was carried out experimentally using a state-of-the-art tri-axial facility at the Colorado School of Mines. The experimental set-up is capable of the simultaneous acquisition of coupled stress, strain, resistivity, acoustic and flow data. Carbon dioxide was used as the pore pressure fluid in these experiments. After a comprehensive characterization of shale samples, experiments were conducted by increasing the pore pressure until condensation occurs while monitoring the mechanical and acoustic properties of the sample to quantify the effect of capillary condensation on the mechanical and acoustic properties of the sample.

Experimental data show a 5% increase in Young's Modulus as condensation occurs. This increase is attributed to the increase in pore stiffness as condensation occurs reinforcing the grain contact. An initial decrease in compressional velocity was observed as pore pressure increases before condensation occurs which is attributed to the expansion of the pore volume when pore pressure increases. After this initial decrease, compressional velocity slightly increases at a pressure around 750 - 800 psi which is close to the condensation pressure. We also observed a noticeable increase in shear velocity when capillary condensation occurs, this could be due to the immobility of the condensed liquid phase at the pore throats. The changes of geomechanical and acoustic signatures were observed at around 750 - 800 psi at 27°C, which is the dew point pressure of the fluid in the nano-pore space of the sample at this temperature. While the unconfined bulk dew point pressure of carbon dioxide at the same temperature is 977 psi. Hence, this study marks the first measurement of the dew point of fluid in nano-pore space and potentially leads to the construction of the phase envelope of fluid under confinement.
Stiff String Casing Design: Tortuosity and Centralisation
Authors: Nobbs Benjamin , Aichinger Florian , Dao Ngoc-Ha , Studer Regis
Published: December 2021

The forces and stresses along casing strings are modeled using a stiff string torque and drag model. The effect of wellbore tortuosity and centralization are quantified in preplanning phase in addition to the effect of 3D orientated casing wear. A realistic case study is presented to show the resulting effect on axial, burst, collapse and Von Mises equivalent (VME) safety factor as well as VME body and connection design envelopes.

While running a tubular downhole, a smooth wellbore is normally assumed when performing a torque and drag calculation. In reality, the inherent tortuosity of the wellbore which is caused by the drilling process can cause significant local doglegs. When applying a soft-string torque and drag model, the stiffness, radial clearance and high frequency surveys needed to fully model local doglegs are rarely modeled. The stiff string torque and drag and buckling model can model these effects, as well as the addition of rigid and flexible centralisers. This study involves the comparison of different casing design load cases, under different centralizer programs and tortuosity taking into account a 3D orientated casing wear.

The results show that there can be significant differences in overall axial stress depending on the centraliser program and tortuosity used. The soft string model doesn't directly account for bending stress, normally this is estimated using a Bending Stress Magnification Factor (BSMF). In contract the stiff string model can directly calculate the additional bending stress. This additional stress can be particularly prevalent while RIH casing with centralisers and high tortuosity. The reduction in American Petroleum Institute (API) and VME stress envelope is also quantified using a 3D orientated casing wear model. A better understanding of axial stress state reduces risk of well integrity issues.

This paper will show the benefits of using a stiff string model, considering additional contact points, bending stress as well as the benefits of modelling tortuosity and centralizer program early in the design process. During extended reach drilling (ERD) and high-pressure, high temperature (HPHT) wells, this information can be critical when correctly assessing the axial stress state.
Wellbore Cleanness Under Total Losses in Horizontal Wells: The Field Study
Authors: Ruzhnikov Alexey , Echevarria Edgar
Published: December 2021

In the Middle East many of the matured fields have fractured or vugular formations where the drilling is continued without return to a surface. This situation has been commonly interpreted as lack of hole cleaning and high risk of stuck pipe. The manuscript describes a study performed to analyze the hole cleaning while blind drilling horizontal sections.

Most of the losses while drilling across fractured or vugular formations happen sudden, and this represents a risk of formation instability and stuck pipe. Additionally, the cuttings accumulation may lead to a potential pack off. To understand the hole cleaning the annular pressure while drilling was introduced in different sections, what via change of the equivalent static and dynamic densities describes the cutting and cavings accumulation in the annulus. Additionally, the hole cleaning behavior with different fluids pumped through the drillstring (i.e. drilling fluid, water, water with sweeps) was studied.

The proposed study was performed in 4 different fields, 9 wells, across horizontal 6⅛-in. sections with total lost circulation. It was identified that while drilling with full returns ECD vs ESD variations are within 1.5 ppg, those variations are matching with the modeling of hydraulics. Once total losses encountered the variations between ECD and ESD are very low - within 0.2 ppg - indicating that annular friction losses below the loss circulation zone are minimal. This support the theory that all the drilled cuttings are properly lifted from bottom and carried to the karst into the loss circulation zone and not fluctuating above the loss zone. Additionally, minor to no relation found in hole cleaning while drilling with mud or a water with sweeps. This finding also is aligned with the stuck pipe statistics that shows higher incidents of stuck pipe while drilling the with full circulation due to pack off.

The manuscript confirms the theory of the hole cleaning in total lost circulation and application of different hole cleaning practices to improve it. The results of the study can be implemented in any project worldwide.
Unconventional Waste & Flare Gas Recovery System UFGRS in New Circular Economy
Authors: Soliman Mohamed Ahmed , Salu Samusideen A , Al-Aiderous Abdullah Y , Ansari Nisar Ahmad , Al-Hajri Khamis , Mecikar Ellyes
Published: December 2021

Keeping pace with the rest of the world on reducing the greenhouse gas emission, Saudi Aramco embarked on an aggressive program to minimize or cut routine flaring and energy resources used in producing oil and gas through policies, standards and inhouse innovations. The innovative Unconventional Waste & Flare Gas Recovery System UFGRS has supported the corporate strategy to minimize or eliminate routine flaring with minimum CAPEX and OPEX.

This paper present in detail the innovative Ejector Based Unconventional Waste/Flare Gas Recovery System (UFGRS) without using gas compressors. The objective of the project is to eliminate the hydrocarbon gas release to atmosphere for any upset flameout scenario from GOSP-A massive flare & relief system and continuously recover 1.825 Billion Standard Cubic Feet per year (1.825 BSCFY) of valuable purge gas with the lowest CAPEX and OPEX.

Conventional Flare Gas Recovery System (FGRS) using gas compressors is the normal choice deployed in many facilities to recover the routine gas flaring but it was found to have high CAPEX and OPEX (maintenance, high power consumption & labor intensive) compared to the value of the recovered gas. Also, the compressors based FGRS is more complex and less reliable than the ejector (static) based FGRS. In addition, the innovative FGRS is capable of handling high turndown ratios compared to convention compressor based FGRS. Also, additional innovative parts of this idea is the integration with the existing compression system and the use of only static equipment like ejectors, pipes, valves and water seal drums to recover the waste/flare gas. The idea has very low operating cost compared to conventional flare gas recovery systems, apart from significant gas savings.

The unconventional FGRS system was proven successfully in December 2020. The system is currently in operation for 8 months without any interruption and managed to eliminate the total design routine gas flaring rate of 1.825 BSCFY GOSP-A producing facilities. Also, the project resulted in reducing CO2 emission by 106,000 ton/year which positively contributed to the kingdom circular economy initiatives.

To further enhance the ejector based FGRS, a US Patent No. 10,429,067 was granted in October 2019 to utilize the Ejector based FGRS concept for Emergency flare gas recovery. The innovative idea includes utilizing multiple ejectors in parallel with provision of different ejectors operating at different pressures that will allow the system to be used to recover flare gas over a range of different flow rates corresponding to different emergency release scenarios. Also, two new patents are under filing to utilize the liquid as motive fluid instead of the gas.
Crude Oil Storage Tank Refurbishment Duration Optimized by 40% Through Advanced Techniques
Authors: Al Yammahi Saeed Mubarak , Al Kaabi Mohamed Obaid , Al Zaabi Rashid Hamad , Shendge Sachin Ashok , Dizon Manly Vista , Sarkar Ananda
Published: December 2021

Crude storage tanks are an important asset in every oil company. Having adequate storage capacities is important economically and ensure steady supply of oil in the market. Hence, taking a huge tank out of service for refurbishment is technically and safety critical, and as much as putting it back into service on time.

This paper presents an advanced methodology and assessment of tank refurbishment process of large capacity Crude Storage Tanks, in compliance with the International Codes and Standards resulting in optimization of the project schedule by approximately 40% as compared to conventional methodology of refurbishment. By deploying the advance techniques, detailed assessment, and dynamic planning we have been able to accelerate the completion of the project, improve tank availability time without compromising with the Integrity and HSE.
Single-Phase Production Profiling in Conventional Oil Producers Using Fiber-Optic Surveillance
Authors: Panhuis Peter in ‘t , Al Shidhani Lujaina , Al Bahri Sultan , Hemink Gijs
Published: December 2021

The objective of this paper is to demonstrate how both Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) data, acquired using a fiber-optic cable installed and cemented behind a 7" production casing, could be used for single-phase production allocation in two conventional oil producers in the South of the Sultanate of Oman.

DAS data can be processed, time-averaged, and filtered to specific frequency bands, to identify and monitor the acoustic frequencies that are excited by the flow through the perforation tunnels. It will be shown that under certain assumptions, the flow-induced acoustic amplitudes at the perforations can be calibrated and converted into actual flow rates, which allows for continuous production profiling across all intervals of interest.

DTS data, acquired under transient conditions, can also be analyzed using a thermal simulation model, to allocate production to specific perforation intervals, provided an appropriate logging program is followed. DTS is not as good as DAS in capturing dynamic changes to the inflow profile, but does have a deeper depth of investigation and is less sensitive to the geometry of the perforation tunnels or possible flow obstructions in the wellbore. The two technologies are therefore complimentary and are best acquired simultaneously.

This is the first case study in the Sultanate of Oman, where both DAS and DTS data sets were successfully acquired and interpreted for single-phase production profiling in a conventional oil producer with perforated casing. Moreover, it was also the first time in Oman that oriented perforation was achieved with full shot density, through a double perforation run with a slight offset in orientation angle between the two runs.
Application of Artificial Intelligence and Machine Learning to Detect Drilling Anomalies Leading to Stuck Pipe Incidents
Authors: Bimastianto Paulinus Abhyudaya , Khambete Shreepad Purushottam , Alsaadi Hamdan Mohamed , Al Ameri Suhail Mohammed , Couzigou Erwan , Al-Marzouqi Adel A/Rahman , Ameri Fahed Salem Al , Aboulaban Said , Khater Husam , Herve Philippe
Published: December 2021

This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management.

The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters.

During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels.

The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime.

This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.
Eco Friendly Lighting Options for Offshore Installations and Related Design Challenges
Authors: Pragasam Sathishkumar
Published: December 2021

The impact of artificial lighting on birds and animals has been a subject of research for many decades. These studies have established that light pollution from offshore facilities result in disturbing effects on migratory birds and marine animals.

It is understood from the various studies that light emanating from offshore structures attract birds at night and disorient them during their migratory flight. As they are distracted from their flight path, they are either found trapped by the light that they keep circling around the installations or make them to take shelter in the installations, ultimately making them miss their migratory journey. This results in reduced winter survival and reduced reproduction success. Many birds are also found to die eventually because of bird strike or by getting themselves incinerated in flares. Congregation of birds in offshore installations also lead to risks related to helicopter landing / take-off.

It is also observed that artificial light attracts marine species like turtles and fishes, which result in adverse effects like alteration of feeding habits, impacted circadian rhythm and interference with their reproductive function.

From the knowledge of aforementioned disruptive effects of artificial lighting, significant efforts have been made to develop lighting systems for offshore installations that are eco-friendly. Various means of mitigating the undesirable effects of artificial lighting in offshore installations are being tried in the last couple of decades.
Gas Deviation Factor Calculation Made Easy and Accurate Using an IR 4.0 Tool
Authors: Al-Hajri Nasser M. , Barghouti Akram R. , Ureiga Sulaiman T.
Published: December 2021

Gas deviation factor (z-factor) and other gas reservoir fluid properties, such as formation volume factor, density, and viscosity, are normally obtained from Pressure-Volume-Temperature (PVT) experimental analysis. This process of reservoir fluid characterization usually requires collecting pressurized fluid samples from the wellbore to conduct the experimental work. The scope of this paper will provide an alternative methodology for obtaining the z-factor. An IR 4.0 tool that heavily utilizes software coding was developed. The advanced tool uses the novel apparent molecular weight profiling concept to achieve the paper objective timely and accurately.

The developed tool calculates gas properties based on downhole gradient pressure and temperature data as inputs. The methodology is applicable to dry, wet or condensate gas wells. The gas equation of state is modified to solve numerically for the z-factor using the gradient survey pressure and temperature data. The numerical solution is obtained by applying an iterative computation scheme as described below:A gas apparent molecular weight value is initialized and then gas mixture specific gravity and pseudo-critical properties are calculated.Gas mixture pseudo-reduced properties are calculated from the measured pressure and temperature values at the reservoir depth.A first z-factor value is determined as a function of the pseudo-reduced gas properties.Gas pressure gradient is obtained at the reservoir depth from the survey and used to back-calculate a second z-factor value by applying the modified gas equation of state.Relative error between the two z factor values is then calculated and compared against a low predefined tolerance.The above steps are reiterated at different assumed gas apparent molecular weight values until the predefined tolerance is achieved.

This numerical approach is computerized to perform the highest possible number of iterations and then select the z-factor value corresponding to the minimum error among all iterations. The proposed workflow has been applied on literature data with known reservoir gas properties, from PVT analysis, and showed an excellent prediction performance compared to laboratory analysis with less than 5% error.
Coiled Tubing Workflow Leads to Successful Completion of Multistage Fracturing in Extended-Reach Wells of Aishwarya Field, Barmer, India
Authors: Jahn Felix , Dehingia Madhurjya , Singh Ishaan , Bandyopadhyay Bholanath , Agarwal Arpit , Singh Atul
Published: December 2021

Coiled tubing (CT) was used to perform multistage fracturing treatments from the CT-tubing annulus in extended-reach wells of Aishwarya Field, Barmer, India. The wells were completed with chrome completion and included multiple fracturing sleeves. With peculiar challenges faced, solutions and lessons learnt are herein captured. In particular, casing deformation was observed in transverse wells, for which the workflow was developed so the wells with post-fracturing casing deformation could be completed and delivered for production.

During the initial phase of the campaign. CT got stuck eight times after fracturing due to casing deformation. In three instances, the bottomhole assembly was left in the hole, and twice the CT was cut for recovery. After the workflow was implemented, no CT stuck incidents occurred due to casing deformation, and all 16 transverse wells in the campaign were delivered successfully.

This study highlights the importance of differentiating between transverse and longitudinal wells while understanding their implications. In wells where casing deformation can occur, the workflow for CT-assisted multistage fracturing (MSF) operations must be adjusted. A smaller outside diameter (OD) shifting tool needs to be used without a packer assembly, and the CT cannot stay in the well during fracturing.
Lessons Learned From the World's Largest Continuous Onshore and Offshore 3D Seismic Survey
Authors: Al Mesaabi Saif Ali , Cambois Guillaume Marie , Cowell James , Arnold David , Boukhanfra Mohamed Fawzi , Al Karbi Saeed Hamad , Mahgoub Mohamed , Obaid Khalid
Published: December 2021

In 2017 ADNOC decided to cover the entire Abu Dhabi Emirate, onshore and offshore, with high- resolution and high-fold 3D seismic. Acquisition of the world's largest continuous seismic survey started in late 2018 and is around 77% complete at the time of writing. Data processing is well under way and interpretation of the first delivered 3D cubes is ongoing. Now is an opportune time to review the status of this gigantic project and draw preliminary lessons.

Comparison with legacy data shows a massive improvement in deep imaging, which was one of the main objectives of this survey. The basement can clearly be interpreted, while it is hardly visible on legacy data being covered with high energy multiples. A thorough analysis demonstrated that increased offset is the main reason for the uplift. The large fold and the low frequency sweep also help recover signal down to 3 Hz. This extends the bandwidth in the low frequencies by one to two octaves compared to legacy data, which tremendously benefits structural interpretation and stratigraphic inversion.
Field Experience from Robot Tests on Drill Floor and Pipe Deck
Authors: Raunholt Lars , Meissner Siegfried , Kverneland Ole Gabriel Johan
Published: December 2021

The objective of this paper is to present results from extensive testing of fully robotic drilling and pipe handling operations in the drilling industry, including several robots and tests on both land and offshore.

Throughout the last eight years, all-electric, heavy-duty drilling and pipe handling robots of up to seven tons capacity have been tested and piloted on dedicated test facilities, land rigs and offshore rigs. The robotic equipment includes drill floor robot, pipe handler robot, robotic roughneck and pipe deck robot with the purpose of removing the people from the drill floor, derrick and the pipe deck. The testing and qualification have been conducted in phases and in a cooperation between equipment manufacturer, rig contractors and operating companies.

The industry has great expectations with the introduction of robotics for red zone management and eliminating all manual operations and human exposure to heavy machinery. Expected value would be a substantial saving in rig days due to fast, precise and consistent operations and removal of people out of harm's way. In addition to improved safety, reduced OPEX, less downtime and faster installation, the robotics systems lower the noise and the carbon footprint due to higher energy efficiency and less GHG emissions. Also, the precise motion control of robots enables digitalization of the drilling process.

The testing of robots in drilling applications has been done with the purpose of testing and qualifying the technology, as well as gaining experience with performance, reliability, maintainability, safety, and value to the users. This paper presents performance data from robot operations including both single robots and full system operations, such as tripping and stand-building. Reliability of electric robots in hostile environment is analyzed with regards to field experience from land rig drilling and offshore operations. Finally, the value to the users is substantiated.

The paper provides unique results and experience from the longest and broadest tests of heavy-duty all-electric robots in the drilling environment. It therefore provides valuable input for decisions of future use of industrialized robots in the oil and gas upstream industry.
Asphaltene Management Leading to Significant Reduction of Production Deferment Through New Technology Trial MFC Tool
Authors: Narwal Tushar , Kumar Kamlesh , Alias Zaal , Agrawal Pankaj , Abri Zahir , Al Hadhrami Abdullah , Al Kindi Abdulbaqi , White John , Asidin Azlan
Published: December 2021

In Southern Oman, PDO is producing from several high pressure (500-1000 bar), deep (3-5 km) and sour fields (1-10 mol % H2S). Over time, wells from one field (S A3) started having asphaltene deposition in the wellbore. Recently, the impact on production became severe resulting in high deferment, increased HSE exposure with plugging and high intervention costs.

Asset team kicked off an asphaltene management project to tackle this problem, with one initiative being a field trial of a new technology, Magnetic Fluid Conditioner (MFC) to avoid/delay asphaltene plugging in the wellbore. This paper discusses the asphaltene management strategy and field trial results from this new tool deployed to prevent/delay asphaltene deposition.
Opportunities and Challenges in Converting Existing Natural Gas Infrastructure for Hydrogen Operation
Authors: Adam Peter
Published: December 2021

Hydrogen holds enormous potential in helping the world achieve its decarbonization goals and is set to play a key role in the Energy Transition. However, two central building blocks are needed to make the hydrogen economy a reality: 1) a sufficient source of emissions-free (i.e., blue or green) hydrogen production and 2) a needs-based transportation and storage network that can reliably and cost-effectively supply hydrogen to end-users.

Given the high costs associated with developing new transportation infrastructure, many governments, pipeline operators, and regulatory bodies have begun exploring if it is both possible and economical to convert existing natural gas (i.e., methane) infrastructure for hydrogen operation. This paper outlines opportunities and technical challenges associated with such an endeavor – with a particular focus on adaptation requirements for rotating equipment/compressor drive trains and metallurgical and integrity considerations for pipelines.
Discrete Fracture Model for Hydro-Mechanical Coupling in Fractured Reservoirs
Authors: He Xupeng , Qiao Tian , Alsinan Marwa , Kwak Hyung , Hoteit Hussein
Published: December 2021

The process of coupled flow and mechanics occurs in various environmental and energy applications, including conventional and unconventional fractured reservoirs. This work establishes a new formulation for modeling hydro-mechanical coupling in fractured reservoirs. The discrete-fracture model (DFM), in which the porous matrix and fractures are represented explicitly in the form of unstructured grid, has been widely used to describe fluid flow in fractured formations. In this work, we extend the DFM approach for modeling coupled flow-mechanics process, in which flow problems are solved using the multipoint flux approximation (MPFA) method, and mechanics problems are solved using the multipoint stress approximation (MPSA) method. The coupled flow-mechanics problems share the same computational grid to avoid projection issues and allow for convenient exchange between them. We model the fracture mechanical behavior as a two-surface contact problem. The resulting coupled system of nonlinear equations is solved in a fully-implicit manner. The accuracy and generality of the numerical implementation are accessed using cases with analytical solutions, which shows an excellent match. We then apply the methodology to more complex cases to demonstrate its general applicability. We also investigate the geomechanical influence on fracture permeability change using 2D rock fractures. This work introduces a novel formulation for modeling the coupled flow-mechanics process in fractured reservoirs, and can be readily implemented in reservoir characterization workflow.
Machine Learning in Computer Vision Software for Geomechanics Modeling
Authors: Noufal Abdelwahab , Sreekantan Jaijith , Belmeskine Rachid , Amri Mohamed , Benaichouche Abed
Published: December 2021

AI-GEM (Artificial Intelligence of Geomechanics Earth Modelling) tool aims to detect the geomechanical features, especially the elastic parameters and stresses. Characterizing the wellbore instability issues is one of the factors increases cost of drilling and creating an AI-based tool will enhance and present a real-time solution for wellbore instability. These features are usually interpreted manually, depending on the experience and usually impacted by inconsistencies due to biased or unexperienced interpreters. Therefore, there is a need for a robust automatic or semiautomatic approach to reduce time, manual efficiency and consistency.

The range of Geomechanics issues is wide and interfaces with many other upstream disciplines (e.g., Petrophysics, Geophysics, Production Geology, Drilling and Reservoir Engineering). Safe and effective field operation is built on the understanding and implementation of the subsurface in-situ stress state throughout the life of the field; the quantification of key subsurface uncertainties through well thought-out data gathering and characterization programs. The integration with appropriate Geomechanics modelling and the field surveillance /monitoring strategy.

There are two major aspects that must be addressed during the design phase of any Geomechanics project. The first and most important is developing a realistic estimate of the expected mechanical behaviour of the rocks and its potential response as a result of drilling. The second is to design an economic, safe well and support method for the determined rocks behaviour. The design process begins with the feasibility study followed by preliminary design, the detail design, tender design and throughout the construction. The design is constantly updated during each phase as more information becomes available and this requires the involvement of Geologists, Engineers and Subject Matter Expert throughout the phases of a project. A central concern for all geomechanical designs is the well-rock interaction, which is not only includes the final state but also the transient effects of the well processes as well as time and stress of the dependent rock properties.

The end-to-end workflow to achieve the mechanical earth model is automated, guided and orchestrated with the help of machine learning framework such as recommendation engine for offset well data, prediction of well logs, and optimization for all calibration with existing test results, enabling end users to run sensitivity and scenario analysis so on and so forth.