bluuebunny/crossref_metadata_2025 huggingface dataset:
Delineating Karsts, Small-Scale Faults, and Fractures by Using a Global Stratigraphic Framework to Integrate Conventional Seismic Attributes with Diffraction Imaging in a Giant Offshore Field, Abu Dhabi.
Authors: Goraya Yassar , Felasi Ali Al , Daynac Nicolas , Walley Stuart , Dupont Marc-Antoine , Inoue Hiroyuki , Mubarak Al Khamiri Ahmed , Hindi Alia Hasan
Published: October 2022

Poor resolution and signal-to-noise ratio have often been key factors impacting an interpreter’s ability to directly delineate subsurface features from seismic data. Improvements in full azimuth, high-density acquisition and in diffraction imaging, have the potential to reveal greater subsurface detail. To date, the application of diffraction imaging has in part been limited by the methods available to parse and analyze that same data. For instance, visualization of diffraction images on time and or depth slices may show patterns, but as these slices can cut through successive seismic reflectors, they tend not to be geologically meaningful. An approach is described that uses machine automation to rapidly incorporate diffraction images into a full-volume 3D seismic interpretation. Delineation of key stratigraphic surfaces is driven by stacking patterns and stratigraphic terminations and performed in both structural and Wheeler domains. Stratal slicing is a highly flexible and rapid method of generating chronostratigraphic surfaces. These chronostratigraphic surfaces can be extracted at sub-sample resolution and therefore accurately matched to well log responses which typically fall well below the resolution of a seismic dataset. The diffraction image is then projected onto a series of chronostratigraphic surfaces, allowing the interpreter to parse through and compare diffraction data directly with conventional seismic attributes at the same chronostratigraphic layer. The novel approach described has been used to demonstrate both the value of diffraction imaging and the importance of using a global full volume 3D seismic interpretation when identifying features such as karsts and small-scale faults and fractures. When applied to a recently processed high-density wide azimuth seismic survey, the workflow was able to seamlessly integrate diffraction images to provide improved confidence in the delineation of karsts and other collapse features that can pose a drilling hazard within the Giant Field, offshore United Arab Emirates.
Subtle Fault Prediction Technique Based on the Integration of Deep Learning and Seismic Spectral Decomposition
Authors: Qiang Li , Xin Chen , Dengyi Xiao , Min Zhao , Qunli Qi , Jianfang Yang , Xiaoliang Li , Bo Peng , Fuli An , Bo Wang , Xiaoli Gao , Chen Yang
Published: October 2022

Faults often control the movement and aggregation of oil and gas. With the development of oil fields, the role of subtle faults is becoming more and more important. The accuracy of fault interpretation directly affects the direction of exploration and development. However, due to the limitation of the seismic resolution, it is hard to identify these faults according to routine methods such as coherence, variance, curvature, etc. To overcome such kind of challenge and better match the demand for fine fault identification, a method integrated deep learning and spectral decomposition was proposed.
Elimination of Pilot Hole and Successful Landing in Sweet Zone Using Ultra-Deep Resistivity Mapping in a Mature Field of Kuwait: Case Study
Authors: Rane Nitin , Al-Duraia Ebraheem , Bojarahs Khalid , Mudavakkat Anandan , Dashti Reham Abbas H , Hassan Jassim Mohammad , Al- Khaldy Ali D , Hassan Abdulaziz , Al-Hindi Khalid Ali , Al-Attar Tarek Fathy , Alhajri Nasser F. , Farhi Nadir , Kharitonov Alexander , Samie Mohamed , Clarion Benjamin , Abdulkarim Anar
Published: October 2022

The Great Burgan reservoir is the largest sandstone oilfield in the world, it has been developed and produced since the 1930s. Historically developed through deviated wells, a new project of horizontal wells was initiated recently to produce from the UB3 reservoir unit. A pilot hole is usually required to identify the presence of productive sublayers and the depth of the oil-water contact (OWC), which must be avoided in the horizontal section. Elimination of the pilot hole would help to minimize the time and cost of development (Al Khalifa et al. 2020).

The azimuthal ultra-deep resistivity mapping service (UDR) has proven its capability to eliminate the need for pilot holes by mapping reservoir boundaries and OWC on the fly, earlier than with traditional methods. This facilitates real-time geosteering to land the well in a single drilling run in the productive zone. Additionally, it helps to reduce non-productive time by making it easier to stop drilling and set casing above a target layer and to help optimize future well planning in field development.

A feasibility study performed on offset wells showed promising potential from application of this method in the UB3.

Real-time UDR geomapping detected multiple thin sand lenses on top of UB3 but showed that they were not of commercial capacity. The decision was made to continue drilling deeper for a larger sand layer. The UDR detected a massive sand below the smaller lenses and the well was landed in it. Early mapping also helped to optimize the landing with the desired inclination and dog-leg severity. The OWC was detected ~35 ft TVD below the landing point.

Without UDR it would have been impossible to detect the OWC and very challenging to perform an accurate landing. The target could have been missed by landing either too shallow or too deep or with the wrong inclination.

Following the landing of the well the lateral section was drilled through upper and lower lobes of the massive sand with a total cut of 1649 ft MD. This comprised 450 ft MD of upper lobe, 350 ft MD of transition interval, and 637 ft MD of lower lobe inside BU3, with an average porosity of 30 p.u. and a water saturation of less than 10%. Formation pressure tests measured mobility of up to ~3.4 D/cp.

This case study demonstrates that utilization of the ultra-deep resistivity mapping service enabled a new approach to drilling lateral wells in the Burgan field development, improving reservoir insight and reducing well drilling time and cost.
Geomechanical Modelling to Identify Sub-Seismic Faults and Fractures Characteristics Using Elastic Dislocation (ED) Theory: A Case Study from Potwar Foldbelt, Upper Indus Basin (UIB) of Pakistan
Authors: Khan Imran , Ahmad Shakeel , Shahzad Rana Faisal , Qureshi Muhammad Akram , Khan Zahoor , Ahmad Shabeer , Awan Uzair Hamid
Published: October 2022

The study will address the failure reasons of wells and point out the high-density fracture zones, to drain out the remaining hydrocarbons in the field. A robust 3D geological model was developed based on 3D seismic interpretation. The rock mechanical properties of carbonates were incorporated. The total strain i.e., the background or remote strain (Bulk deformation) and the strain from displacement along the fault surfaces are mapped to each segment/element of the generated fault surfaces. This total strain calculates stresses and the failure for deformation surface. The geomechanical model based on Elastic Dislocation (ED) theory identified strain fields on horizon surface / observation grids and then finally fractures corridor and their characteristics i.e. distribution, orientation.

Fault planes generated from interpretation play a major role in the ED method for fracture analysis. The fault surface consists of an array of panels, each contributing to the ED equation calculation. The main outcome is the sub-seismic faults and fractures identification around larger faults on the horizontal observation medium. The identified fractures corridors characteristics, distribution and orientation changes along the strike of the major fault system. In the developed ED model the predicated fractures system are parallel to the major reverse fault direction, but oblique fractures corridor is also observed along the middle segments, aligning with observed variations in structure dip. The crestal portion of the anticline has a higher density of fractures than the rim.

The ED modelling fractures results were verified against FMI data of the targeted horizon, which demonstrated that the wells which were drilled in high-density fracture zones (modelled) have produced hydrocarbons and vice versa. There is a correlation between modelled results with image logs and well-testing results (DST's), which increases the reliance on the ED theory's ability to correctly identify small-scale (sub-seismic) fractures, joints and faults system.

The Eocene carbonate reservoirs have low primary porosity and permeability. The productivity of these reservoirs is dependent on permeable natural fractures and sub-seismic faults. The identification of these features is a major problem before drilling while, conventional techniques do not provide optimum solutions to their understanding. A case study of compressional tectonic regime in Himalayan fold & thrust belts is presented here, where an integrated approach is applied in the form of geomechanical modelling, which is built on the ED theory provide a reliable base for well planning.
Time-Lapse Walkaway VSP Analysis Using Advanced 4D Attributes to Monitor CO2 WAG EOR Pilot in a Carbonate Field Onshore Abu Dhabi
Authors: Mohamed Moza , Humphries Mary , Waqas Muhammad , Ahmed Jahan , Ofowena Frank , Alhamedi Aysha , Bailey James
Published: October 2022

Reservoir monitoring is a key initiative for efficient management and development of hydrocarbon reservoirs. Time-lapse feasibility studies are available in literature, however; actual time-lapse seismic surveys are very limited in Abu Dhabi.

A time-lapse Walkaway Vertical Seismic Profile (VSP) was acquired as part of the reservoir monitoring plan during a Water Alternation Gas (WAG) Enhanced Oil Recovery (EOR) pilot in an onshore carbonate field in UAE. The purpose of the study is to monitor the injected fluid in the reservoir. A 4D feasibility study predicted saturation changes >50% after 6 years of water and CO2 injection resulting in >10% change in reflection amplitude. Time-lapse Pulsed Neutron logs showed saturation changes of about 25% for both water and CO2 after 3 years of injection.

Walkaway VSP survey design was performed but in practice optimum receiver depths could not be achieved due to well tubing resulting in reduced lateral image extent. Three surveys were recently acquired: Baseline survey after 6 months of CO2 injection, Monitor-1 after 6 months of water injection and Monitor-2 after 3 years of WAG injection.

The surveys achieved excellent data quality. There were some small inconsistences between all the surveys that were revealed and partially resolved during detailed parallel pre-image re-processing of the three datasets. 4D image attribute analysis recovered a subtle time-lapse response. The time-lapse response was compared to the observed CO2 and water saturation differences and some correlation was observed. Further modeling is required to investigate these small observed differences. Future monitors will take into account lessons from these surveys.

It is hoped that this study will provide confidence to operators for deployment of borehole seismic survey technology for future time-lapse monitoring in carbonate reservoirs. Workflows applied in this study will provide a reference point for future geophone or fiber VSP surveys for 4D monitoring. This study also highlights significance of source consistency and quality control that should be performed at wellsite during acquisition.
Determination of Caprock Porosity and Permeability Using Drill Cuttings and Core Samples of a Potential CO2 Storage Site – A Case Study
Authors: Sazali Wan Muhammad Luqman bin , Busch Andreas
Published: October 2022

Carbon capture and storage (CCS) provides a safe option to reduce carbon footprint on a large scale. Here, carbon dioxide (CO2) is stored in a reservoir formation, overlain by a seal with low permeability and high capillary entry pressure. Understanding CO2 migration through the seal is one of the main components to assess caprock integrity, thereby ensuring safe and long-term CO2 containment and storage. This research is a combined and detailed study of caprock porosity and permeability using core and well log data to overcome a major issue: Coring and logging operation are expensive, and rarely done in caprocks. Drill cuttings, however, are available as byproduct of all drilled wells. By studying the caprock porosity and permeability of a potential CO2 storage site, this study aims to develop porosity/permeability relationships which can be used as input to predict matrix migration and capillary leakage to ensure permanent storage of CO2. In this paper, drill cuttings and core samples, which were obtained from a potential CO2 storage site, went through a series of laboratory measurements to determine porosity and permeability. This includes mercury injection capillary pressure (MICP), unsteady state pulse decay permeability on plug samples, and poro-permeameter measurements on crushed samples. Preliminary results have shown that drill cuttings produce higher porosity and permeability values than core samples. Nevertheless, further analysis is needed to establish relationships and correlations between drill cuttings and core samples. By applying a multi-method approach, resulting in trends for porosity and permeability, we may be able to reduce the operational costs of coring by using drill cuttings as alternatives and at the same time help de-risking CO2 storage projects for wider scale of deployment.
Spending Less and Getting More: Impact of 3D Seismic Processing and Inversion in an HPHT Carbonate Field Development With Only Single Well & Legacy 3D Seismic
Authors: Lai Chee Leong , Choo Matthew , Fahmi Fariz
Published: October 2022

This paper will focus on a High-Pressure High-Temperature (HPHT) carbonate gas field in Central Luconia, Field X, offshore Sarawak, Malaysia. Discovery of field X was made based on a 2009 3D legacy Pre-Stack Time Migration (PreSTM) seismic data and an exploration well drilled in 2013. The main challenge for this field is to move from the exploration phase into the field development phase, without acquiring additional seismic or well data. One of the ways forward, is to extract new information via seismic data reprocessing. The reasons for this are the cost of seismic reprocessing is marginal compared to drilling an appraisal well and there is significant improvement in the seismic processing and seismic inversion technology over the last decade. Full Waveform Inversion (FWI), combined with other latest processing technology, are the key workflows in this seismic reprocessing that determines the quality of the results. A quantitative interpretation (QI) study was subsequently performed to conclude the seismic study and enhance reservoir understanding in preparation for field development planning.
FWI Using Reflections in Shallow Waters Offshore Abu Dhabi
Authors: Hermant Olivier , Khakimov Oleg , Barajas Carlos , Hutahayan Bastian , Mahgoub Mohamed , Cambois Guillaume
Published: October 2022

The Arabian Gulf near-surface geology is complex, with extremely shallow waters and a hard water bottom generating high amplitude short period multiples and thinly bedded high and low velocity layers creating high apparent anisotropy in the bandwidth of seismic surveys. Obtaining an accurate description of the velocity variations in the near-surface and at intermediate depths is a necessity for reliable imaging and positioning of the reservoir layers located underneath. We propose a two-step full-waveform inversion (FWI) of ocean-bottom node (OBN) seismic data from offshore Abu Dhabi. We update a velocity model, using both diving and reflected waves, to reach the required depth of penetration.

FWI has become an industry standard for velocity model building. However, due to the oscillatory nature of seismic data, FWI is known to be subject to cycle-skipping, where the inversion process falls into a local minimum. This risk is mitigated by using an accurate enough initial model and the use of low frequencies. In the shallow waters of offshore Abu Dhabi, near-offset data suffer from strong mud-roll and guided-wave energy that are not properly modeled with acoustic FWI. We exclude these offsets from the input data and use diving waves, starting at 3.5Hz, to update the near surface.

The diving waves penetration is limited to approximately one kilometer in this area and corresponds to the base of a shallow high velocity layer. To reconcile the kinematics of reflected waves, travelling mostly vertically and used for imaging, and diving waves, travelling mostly horizontally, and used in the velocity update, we need an accurate estimation of the anisotropy. This is obtained using Backus averaging from available well logs.

For deeper updates, the data are processed to remove the mud-roll and guided wave energy. This allows for the inclusion of reflections and near offsets. The FWI update is performed to 10Hz and penetrates about 3km into the sub-surface.

We applied this FWI workflow to a recent node survey acquired offshore Abu Dhabi. The velocity model obtained follows the main geological structures and accurately describes the velocity variations in the shallow sub-surface. The estimation of anisotropy is important to ensure good convergence of the FWI and for imaging and vertical positioning of the migrated events. The reverse-time migration (RTM) image obtained with the updated model shows improved focusing and simplified depth structures compared to the RTM image obtained with the smooth initial model.

To the best of our knowledge, this is the first successful implementation of FWI, here combining diving and reflected waves, on a dataset from offshore Abu Dhabi. It is a step towards resolving buried anomalies such as karst features, that cause imaging distortions at deeper reservoir levels.
Interpreting Subtle Faults and Multiple Horizons Layers Using Machine Learning and Data Driven Approach
Authors: Syed Bilal Saeed , Mukherjee Jagannath , Ditia Tadeo , Seliem Abdulla , Al Kobaisi Abdulla Saad , Andrews Ben , Jaramillo Alejandro , Norlund Phil
Published: October 2022

The Lower Cretaceous Thamama Group is one of the proven and productive hydrocarbons bearing intervals within Abu Dhabi area. The geological setting of Thamama Group is complex due to the nature of its structural evolution in a strike-slip regime with high-angle fault and subtle vertical displacement. Additionally, the stratigraphy of Thamama Group consists of multiple stacks of Carbonate reservoirs which are separated by various Anhydrite layers. Some of the carbonate layer cannot be interpreted properly due to data resolution and quality of the seismic reflector.

Due to the geological complexities, using the existing 3D Seismic Data to interpret faults and horizons within due time frame for critical decision making can be extremely challenging. Vertical displacement of the fault often cannot be easily recognized from seismic section and auto-tracking horizon interpretation doesn't produce a good result due to lack of reflector continuity.

In the specific area of this study, the main geological challenge was to interpret those N45W subtle faults developed at the central region of the seismic volume. To address this challenge, we first applied one of the existing machine-learning models proposed by Jiang and Norlund (2020) to pre-train a multi-channel convolutional neural network model with a set of synthetic seismic volumes (over 200 different cases), that resembles multiple geological scenarios with similar structural characteristics to those observed in the study area.

We utilize a new point-based method that leverages a network analysis technique to automatically extract fault surfaces from fault imaging volumes.

For horizon interpretation we use a new Assisted Horizon Interpretation workflow we are developing. This approach is good for identifying and tracking larger, more continuous surfaces throughout a seismic volume. This results in many output horizons that fill the entire seismic cube, we refer to this as “Dense Horizon” extraction. This portion of the project uses a deterministic approach for identifying & tracking patches throughout the entire seismic volume & then uses several automated and manual tools to join those patches to generate complete surfaces.

The results achieved with this study helped to improve the quality of faults and horizon generation in complex geological area with improved efficiency and accuracy.
New Scheme for Sonic Verticalization in a Highly Deviated Well, in a Complex Carbonate Field, Onshore Abu Dhabi, UAE
Authors: Gao Xiaoli , Grover Anurag , Wang Ailing , Ahmed Jahan Zeb , Zhao Wenhao , Al Mesmari Abrar , Lv Lu , Deng Anxiong
Published: October 2022

Reliable sonic and density logs are key inputs for seismic driven reservoir characterization. Anisotropic surface vertical velocities are essential for seismic well ties, estimation of wavelets and low frequency modeling. These help us to understand the relationship between reservoir and acoustic properties. In this study, sonic logs were acquired in highly deviated wells penetrating highly anisotropic shale formations. These logs needed corrections before integrating in the reservoir characterization workflows.

In allusion to problem existed in traditional methods, an iterative inversion method based on multi-well models composed of adjacent vertical and deviated wells was proposed. Well data was optimized after matching depth of vertical and deviated wells. Thomson approximation formula was re-derived based on multivariate linear fitting and least square coefficient matrix was constructed to obtain delta and epsilon respectively. Best Thomsen parameters were obtained to address shale anisotropy.

The results show that the Nahr-Umr shale formation have strong anisotropy for highly deviated wells in the field of study. The correction was not required for deviation less than 20 degrees. The correction gradually increases and reaches as much as 20% to 30% at 60 degrees. It was observed that there is a huge difference between the measured velocity of highly deviated wells and the true vertical velocity in the shale formation. This anisotropy can affect seismic imaging, time-depth conversion, AVO, well-seismic calibration. Therefore, sonic log correction is necessary for highly deviated wells in the shale formations. Corrected sonic logs in deviated wells provide accurate acoustic velocities for further reservoir characterization & seismic inversion studies. The real acoustic data of deviated wells can be obtained from shale formations with strong anisotropy such as Nahr-Umr and Laffan formations.
Reservoir Property Distribution Constrained with 3D Seismic Data in a Tight Carbonate Reservoir: A Machine Learning Based Approach
Authors: Salib Mina Sameh , Alzaabi Naema Obaid , Ahmed Jahan Zeb , Gacem Mohamed Tarik , Abd Rashid Atiqurrahman Bin , Albreiki Warda Salem , Ba Hussain Ali Hasan , Alkarbi Abdulrahman Mabrook , Al Qawadi Hajir Mohamed
Published: October 2022

When dealing with a tight reservoir of basinal settings, depositional facies can show a lot of overlap in their properties. This onshore tight carbonate reservoir, is almost exclusively composed of wackestones to mudstones. The main reservoir quality influencer is the diagenetic overprint reflected in the cementation and dissolution phases, and directly influencing the porosity variation. This made the classical generation of facies trend maps to constrain the properties less reliable, due to poor property discrimination. It demanded the utilization of seismic data to attempt to constrain the properties as much as possible. With the static rock types being sensitive to porosity variation, and porosity having a good correlation to the acoustic impedance, the seismic data may provide key information to obtain lateral trends guiding the properties distribution. A supervised neural network approach was used to provide a link between the lateral variation of reservoir quality and seismic data. The workflow relied on combining deterministic inversion and volume attributes to extract the information. This was followed by multi-attribute analysis to find the best combination of attributes to input into the neural network. Four main challenges had to be tackled. Firstly, an acquisition footprint masking the signal in the seismic data had to be reduced. Secondly, the integration of the seismic inversion output and well information in the modeling workflow. Thirdly, generating and finding the best combination of seismic attributes to fit the given logs. Lastly, adapting the low resolution of the seismic data to the subzones of the reservoir in order to capture the vertical variation. The resulting predicted impedance cube is validated with actual porosity data from wells. The result has proven to be beneficial in building an integrated 3D property model, giving more confidence in the predictability of the properties in horizontal wells and volume estimates. This is seen in the blind tests applied, which show a good alignment between actual and predicted properties.
Innovative Sealant for Mitigating Methane Gas Emission in Abandoned Oil and Gas Wells
Authors: Heras Murcia Daniel , Reda Taha Mahmoud , Stormont John
Published: October 2022

The traditional solution for capping abandoned oil and gas wells is to fill portions of the open well with cement. However, cement is not ideal for plugging or capping because it often results in an ineffective seal. Cement proves ineffective due to shrinkage, inability to bond to steel casing, degradation over time, and a large carbon footprint in production. To be a solution for greenhouse gas (GHG) emissions from abandoned wellbores, plugging must be effective indefinitely. Moreover, the effectiveness must be documented over time. Thus, the conventional approach of plugging with cement and walking away offers no documented proof of GHG emission mitigation.

In this work we suggest the use of a proprietary polymer-based sealant for plugging and abandonment. The sealant, referred to TSN-20, has superior flowability, excellent bond strength to steel and rock, is very ductile, and is thermally stable. TSN-20 sealant can penetrate, flow into and seal extremely thin microcracks (< 30 microns) where cement and most other sealants cannot flow. These small microcracks are problematic leakage pathways for methane and other GHGs. Further, unlike cement, TSN-20 bonds to steel and rock, is very ductile, thermally stable, and resist harsh downhole conditions, including acids and hydrocarbons. Tests showed TSN-20 can dramatically reduce the flow rate after one sealant injection. TSN-20 sealant can be used to mitigate methane gas emissions in abandoned oil and gas wells.
Enabling Shallow Reservoir Development in Malaysian Deepwater Field Via Statically Underbalanced Mud with MPD Applications
Authors: Lee Magdelene Jia Wern , Mohd Sukri Eskandar , Musa Muhammad Idham Adli , Ong Sheau Hun , Bin Abu Bakar Muhammad Aizat , Binti M. Safari Lai Harni Farihah
Published: October 2022

Managed pressure drilling (MPD) technology is a widely accepted technology for wells with narrow pore- pressure/fracture-pressure (PP/FP) gradient window. Whilst MPD is often associated with high capital expenditures (CAPEX) that further erodes the project economics especially for marginal reserves, it does not limit its application since MPD technology can considerably reduce non-productive time (NPT). Most importantly, it is the key enabler to access the previously conventionally un-drillable reservoir target, generating significant revenues to the organization.

As the plan new development well cannot be drilled conventionally by using overbalance mud, numerous dynamic drilling simulations were performed utilizing drilling engineering software to ascertain its drill- ability via constant bottomhole pressure (CBHP) MPD with statically underbalanced mud. As an input, detailed surface equipment specifications, i.e., surface MPD line up and well control equipment are important to ensure successful drilling simulations. Sensitivity analysis using surface back pressure and drilling mud density are performed to determine maximum drillable footage for each hole section. As proper cementing is paramount for well barrier, well drill-ability is further assessed with managed pressure cementing (MPC) simulation to ensure the well is operationally executable.

This paper presents a case history on operations performed in-house well engineering design for a complex and marginal deepwater development field during front end stage with narrow PP/FP gradient window (less than 1ppg drillable mud window) and elaborate on extensive work done for MPD engineering planning procedures in designing a fit-for-purpose well design.
Machine Learning Model for Drilling Equipment Recommender System for Improved Decision Making and Optimum Performance
Authors: Kloucha Chakib K. , El Yossef Bassem S. , Al Hamlawi Imad , M Salim Muzahidin , Pausin Wiliem , Pal Anik , Mustapha Hussein , Shah Soumil , Hussein Ahmad Naim
Published: October 2022

The oil industry, in its constant strive to maximize gains out of operational data is constantly exploring new horizons where to combine the latest advances in data science and digitalization, into the areas where key decisions to drive economical and operational decisions reside with an aim at optimizing the capital expenditure through sound decision making. High volume operational data has been recognized as hiding many opportunities where the captured details these repositories that include real time logs and bit run summaries, provide a clear opportunity where to extract insights to support optimized decisions in terms of equipment selection to achieve the desired operational objectives.

Current possibilities within data science have opened the possibilities through viable solutions, which in this case, aims at providing advise on which equipment in terms of BHA and Bits to select, that would yield the desired outcome for a drilling run. The whole exercise being based on evidence gathered from previous runs where the details for the equipment, the relevant well characteristics, and the observed rates of penetration and the used parameters, are taken into consideration to provide the optimum combination to be implemented in new runs.

The present study describes the methodology in terms of data utilization, data science method development and solution deployment, with the associated issues that had to be addressed in order to provide a viable solution in terms of data utilization, technical validity and final user utilization, as well as a series of recommendations to be addressed within any such endeavors to assure the value addition.
Real Time Data Analysis Unveils Potential Savings in Drilling Operations Through Invisible Lost Time Automated Tool
Authors: Landaeta Rivas Fernando , Cotten Michael , Bimastianto Paulinus , Khambete Shreepad , Krikor Ara , Al Ameri Suhail , Couzigou Erwan , Al Marzouqi Adel , Pausin Wiliem , Al Shahtoor Mohammad , El Masri Hassan , Shabbir Umer
Published: October 2022

Objectives/Scope: Traditionally Daily Drilling Reports were used as the main source of the data input for Invisible Lost Time calculations with great success. One observed drawback was that the hidden causes that lead to invisible lost time were not identified. This paper outlines the framework to incorporate Real-Time high frequency rig-floor sensor data in the invisible lost time calculation, identification, and reduction exercises. This approach breaks down, downhole activities into smaller, measurable, discrete sub-activities that are measured, benchmarked, and targeted for improvement.

Methods, Procedures, Process: This paper covers three major downhole activities: Drilling, Tripping, and Running Casing, breaking them down into thirty-three sub-activities. A three-year drilling activities dataset is categorized into comparable groups based on drilling rig capabilities and formation characteristics. The sub-activities are then presented on a histogram with the average, P10, P50, and P95 values of each sub-activity determined based on a pre-accepted category. This benchmarking process enabled the generation of the targets that are currently being used for calculating and understanding the invisible lost time causes in the operation.

Results, Observations, Conclusions: revious reporting would simply identify the phase of operation that is creating the invisible lost time. Complementing the existing Invisible Lost Time calculation model with Real-Time data has enabled the understanding of the operational steps that lead to sub-optimal performance. A good example of this would be the drilling phase. The new approach is able to pinpoint the steps in the drilling phase that generates the invisible lost time, for example when post-connection times are excessively exceeding historical norms. Another advantage is the ability to run the report in the middle of the activity and highlight that the current rate of execution is not optimal. This enables the supervisor to investigate the situation and propose solutions to improve performance on the ongoing activity. In addition, this approach has been used successfully for ranking the performance of casing running service companies and helping in the decision-making process for awarding new contracts.
Artificial-Intelligence-Based Well Placement Look Ahead Concept
Authors: Mahiout Mohamed Said , Al Enezi Faisal Naif , Santoso Gagok Imam , Ali Elhaj Mohammed Satti
Published: October 2022

Artificial intelligence (AI) brings many benefits to the oil and gas industry and in particular the automation of the geosteering process with structural prediction ahead of the bit to optimize horizontal well placement and reduce the operational challenges. AI techniques are based on data analysis that learn from data, identify patterns and make predictions without the need of direct human intervention. Recent ultra-deep azimuthal resistivity (UDAR) logging-while-drilling (LWD) tools have reached a significant depth of investigation capabilities to provide real time information that covers a large volume of the rock around the wellbore. The objective of this paper is to provide details on the automated geosteering methodology used to detect formation changes in real time ahead of the bit based on AI approach.

The new automated geosteering approach is based on a suite of real-time enabled pre-drill and data integration workflows. The latter combines UDAR technology with seismic data. The suite of both workflows as sequential process allows for precise well planning and prediction of a structural model ahead of the bit in real-time. At first, the pre-drill workflow consists of data quality assessment, preconditioning, building 3D tectonic model repository and signal consistent 3D stratigraphic model incorporating offset wells which is constrained by tectonic model. During the second phase with the data integration workflow, ultra-deep azimuthal resistivity inversion output, generating synthetic seismic, extracting from 3D seismic volume along well-path, estimating depth mismatch through non-rigid matching, apply displacement to real seismic and model repository. Therefore, enabling a structural prediction ahead of the bit in 2D and 3D and aiding geosteering decisions in real-time.

The new well placement look ahead concept will enable a right strategy that will enhance the geosteering decisions in real-time. Examples highlighting the value of the AI-based well placement look ahead concept are provided in the paper. Finally, this AI-approach will evaluate the potential added value to the well placement activity.

The proposed workflow for look ahead geosteering based on real time integration of LWD measurements with surface seismic is a major step towards unlocking new horizons of providing critical information hundreds of feet ahead of the bit. This proposed technique will allow the reduction of subsurface uncertainty and helps the achievement of geosteering objectives.
Predicting UCS with Neural Network and Benchmarking Actual ROP for Further Drilling Optimization
Authors: Alexeyenko Alexey
Published: October 2022

Rotary/motor drilling operations amount to approximately 30% of total time of well construction or approximately 800 hrs for gas wells studied in this paper. Reducing drilling time by 10% will result in significant time and cost savings, especially if drilling deep horizontal wells. Time and cost reduction becomes particularly valuable when no extra investments in new tools or materials are required. Optimizing the drilling process using real time feedback from drilling rig sensors is one of the options proved successful by long time practice. Artificial intelligence and its tool: neural networks (NN) is justifiably in the petroleum industry's focus to deliver valuable results with a high return and minimum or no investments. The fact that rock hardness reflects on some drilling parameters is well known. It is natural to try and learn how and to what degree rock hardness can be predicted with use of NN, drilling parameters and well logging records. It is also well understood that the smaller the geographical extent of the formation data sample, the more correlation in properties is observed. This study focuses on wells drilled in the same gas field and cased with the same casing design.

This paper summarizes the study of predicting Unconfined Compressive Strength (UCS) while drilling a horizontal well in highly compacted, cemented sandstone with low permeability and low porosity reservoir (unconventional reservoir) with trained NN in MATLAB environment. Methodology and workflow of arriving at the results of the study is provided in extensive details. The initial goal of UCS prediction with NN is to prove the feasibility of proposed workflow and develop a MATLAB function that predicts UCS, utilizing drilling parameters recorded by mud logging unit and drilling rig's sensors and computing expected rate of penetration (ROP) using common published formula. The final goal of the study is to benchmark actual ROP with forecasted ROP with a high degree of certainty (more than 80% correlation), detect drilling troubles, and optimize drilling parameters for balanced performance and safety operations while reducing drilling time for the given footage.

The study substantiate the proposed methodology and workflow predicting UCS of the formation being drilled and provides input for comparing predicted and measured drilling parameters. The study also verifies and selects which drilling parameters have substantial correlation with UCS. Keeping in mind that it is important to optimize the optimization process. Reducing the number of input parameters for the NN model was also a focus of this study. It was found that surface coordinates, measured depth, rate of penetration, rotation speed, surface torque, surface weight on bit, gamma ray and depth related logged measurements of UCS are sufficient to train the NN model with R2=81%. Data from 9 wells was used in this study.
Maximizing Marginal Fields Profitability Via Drilling Multi-Lateral Wells to Reduce Well Count and Well Cost: Dual Lateral Well with Single ESP and Smart Lower Completion
Authors: Mostafa Mahmoud Mohamed Abdelhay , Kwon Sunil , Oh Hyunjung , Al Braiki Ali Mubarak , Al Shkeili Fatema , El Shahat Ayman
Published: October 2022

Expensive drilling is highly impacting the overall marginal field profitability due to the low reserves and short well life. In this paper, we considered the case of a marginal field with multiple reservoirs stacked above each other with a thin dense layer between each pair of reservoirs.

The initial plan was to drill a single horizontal oil producer well across each of reservoirs. Those wells had planned to equip Electric Submersible Pump (ESP) completions to ensure enough surface pressure to pump the produced fluids to the gathering facility, which is far from the well's surface location, in addition to an expected increase in water production in the early stages of well production. To reduce the overall project cost and to overcome all those challenges, collaborative work resulted in agreement on drilling one well with two lateral horizontal holes across two different reservoirs instead of drilling two single wells, which conflicted with regulations that prevent comingled production through a single completion. To solve regulation of comingled production, a simple smart lower completion with Inflow Control Device-Sliding Sleeve Doors (ICD-SSD's) and swell packers was used to control production from both reservoirs whenever required, in addition to using a single ESP completion to produce from both zones together.

This well design helped to reduce the total drilling cost by USD 5 million while achieving the same production rate that was expected from the two individual wells that were originally planned to produce from the two different reservoirs.
Step Change in Drilling Performance Through Surface Automation in Onshore Abu Dhabi
Authors: Al Hamlawi Imad , Creegan Andrew , Ronquillo Nahum , Baptista Luis , Jalbout Mohamed , Al Nuaimi Mouza , Azizuddin Khaja
Published: October 2022

The process of drilling an oil or gas well is inherently challenging due to the unpredictable nature of many of the variables at play. However, the overall process can be broken down into many smaller phases, each of which has a signature of repeatability. By automating the repeatable processes, and by optimizing the inputs that are a function of variable conditions, we can industrialize the drilling process at a higher level and drive toward consistent, high performance. Improving operational inefficiency requires long-term training or special tools to help address downhole issues. This can be costly and requires many man hours. In the pilot reviewed in this paper, a Surface Automation Solution was implemented to improve well construction performance. The Surface Automation Solution was comprised of a Drilling Automation Platform (DAP), a real-time Intelligent Drilling Optimizer (IDO), an automated stick slip mitigation system, and Automation Lifecycle Management (ALM) supporting services.

The Surface Automation Solution showed extraordinary performance, delivering efficient drilling connections, optimum drilling performance and mitigating drilling dysfunction. New records were achieved in every hole section where the system was operated, resulting in 51% overall ROP improvement compared to offsets, and 44% reduction in stick slip severity, translating to 3.2 rig days savings.

In this paper, we will examine how the Surface Automation Solution saves well delivery time by automating drilling activities, mitigating drilling dysfunctions and optimizing parameters to increase ROP on each section. The outcome is measured by the performance, which in this case is time saved. The data shown is the overall macro key performance indicator (KPI) along with the performance at each individual hole section.
GHG Emissions Tracking on Artificial Lift
Authors: Osman Rami , Bermúdez Fernando , Shono Mohammed , Alnahhas Noor , Yazdani Hafsa , Letan Michael
Published: October 2022

This paper demonstrates a novel approach to tracking greenhouse gas emissions of artificial lift equipment, to support data and data driven decision making in delivering a circular carbon economy. Leveraging the outcome of insights and new datasets through implementing nybl's proprietary novel "Science-Based Artificial Intelligence" to Electrical Submersible Pump (ESP) Lift.ai to wells in the context of Environmental Sustainability and Governance (ESG) in real-world applications. Identifying opportunities to integrate advanced data analytics, Machine Learning (ML) and Artificial intelligence (AI) to improve operational efficiency, production, transparency and reducing downtime, and environmental and social impacts of ESPs, which can also be applied to other rotating, reciprocal and cavitational equipment in the future. Utilizing these techniques in a novel approach will increase transparency and allow active, rather than reactive, ESG efforts and enable data-driven decision-making in this space with limited additional Remote Telemetry Units (RTUs) associated costs - with further opportunities for sites where additional RTU related investments are warranted to drive increased insight.
Transforming Maintenance Delivery Through Scaled Deployment of Connected Worker
Authors: Scott Keith David
Published: October 2022

Paper summarises the learnings from 3 years of deploying Connected Worker solutions across Operations, Maintenance, Integrity disciplines, on both Core and Campaign activities, in Oil & Gas in the UKCS (United Kingdom Continental Shelf). The primary purpose is to share that experience with industry in order to support other Operators in deploying Connected Worker solutions. The paper considers aspects such as the architecture of the system, the evolution of workflows, the integration of the Connected Worker solution into a data lake and the subsequent deployment of dashboards and automated insights to the supporting technical and management teams. Case study examples are included to evidence those points.

Connected Worker technologies are discussed in relation to their potential as foundational requirements for higher order technologies that drive predictive maintenance and the optimization of maintenance, operations and integrity management processes. In conclusion, five features of the architecture of a Connected Worker solution are noted as being critical to delivering a solution that adds value to the business. The five are: (1) Ease of use by field personnel, (2) Ability to integrate easily with existing systems of record, (3) A simple means of generating workflows that is usable by all technical personnel, (4) An efficient and elegant approach to the ongoing maintenance of workflows and tasks, and (5) Ease of integration with other corporate data structures.

Proposed approaches to delivering these requirements that have been previously applied and found workable in service are outlined.
Smart Mobile Worker for FCS PM
Authors: Al Kindi Omar Al Khatab , Al Kalbani Fahad Khamis
Published: October 2022

Since 1980s, most process industries have embraced digital technology for better understanding and recourses managing, increase overall efficiencies and improve safety. In fact, the Industrial Automation (IA) field has given increase to the 3rd industrial revolution for nearly half a century. However, since then, IT megatrends have driven a significant growth in storage, information, computing power, and bandwidth. In this environment, the present of the network has enabled an open platform in sharing ideas, tool development and an agile strategy to exponentially speed up the software development effects, new business models and algorithms. Consequently, digital transformation and digitalization are becoming progressively in many industry. Digitalization can be defined as a use of current available digital technologies to enhance the business model or specific process and deliver new value and revenue. It is considered also as an approach that making processes and workflow easier than before and more efficient. Especially in today's tech - driven world, it is really important to adopt a digital culture in order to enhance the overall work and succeed. PDO is pushing the frontier of digitalisation in its maintenance strategies by implementing various modern smart technologies that can make its approach more succeed and effective. Hence, Smart Mobile Worker (SMW) is designed where it gives the site team the capability to log their maintenance observations in a hand-held tablet easily. Then, all the entered reports are transferred immediately to Nibras system where there is a customized design dashboard that gives the first line support team the overall performance of the Field Control System (FCS) along with alerts and notifications for any abnormal behavior. SMW and Nibras system are both integrated to conduct remote maintenance and proactively identify any exists issues in FCS/IPS/FGS system cabinets. This type of platform helps in bringing the expert office capabilities into the field which will help in taking the decision immediately and in short time.