bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence to Enhance Corrosion Under Insulation Inspection
Authors: Amer Ayman , Al-Shehri Ali , Cunningham Vincent , Saiari Hamad , Alshamrany Abdulaziz , Meshaikhis Ali
Published: November 2020

A critical challenge facing the integrity of many assets throughout the oil and gas industry is directly related to corrosion under insulation (CUI). Unfortunately, the lack of adequate inspection technologies adds to this well-known industrial challenge. Presented in this paper is an inspection tool enhanced using Artificial Intelligence (AI) that can provide field inspection engineers with a facility heat map of insulated asset integrity allowing inspection prioritization.

The approach used in this research, and presented here, was to enhance the output of already known and field approved thermographic technologies using a purpose built AI based on Machine Learning (ML). By examining the progression of thermal images, captured over time (<20 minutes), corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. Additional benefits to this technique include enhanced safety through remote inspection and additional cost savings from monitoring assets online.

To develop and verify the CUI technology results from in-house laboratory tests followed by field validation outcomes will be presented. Laboratory trials were carried out using a series of insulated field assets with different levels of degradation and structural integrity set up to mimic the thermal behavior of in-process assets. This initial feasibility study allowed the definition of key parameters required to build an effective ML model. Following in-house trials a series of field tests and visual verification was performed on both hot and cold insulated assets to gather a sufficient amount of datasets to train the predictive algorithm. To enhance this learning process, synthetic data was created based on real field asset configurations and operating parameters. Finally, during the technology validation phase, again on field assets, the AI technique coupled with a commercial field approved thermographic camera returned a predictive accuracy in the range of 85 – 90%.

The work presented in this paper provides a solution for the current lack of technologies to monitor the presence of CUI by enabling and enhancing the output from already known and field approved technologies, such as thermography, using AI. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the complexity of asset preparation (scaffolding) and downtime.
Novel Methods for Production Data Forecast Utilizing Machine Learning and Dynamic Mode Decomposition
Authors: Gryzlov Anton , Safonov Sergey , Alkhalaf Muqbil , Arsalan Muhammad
Published: November 2020

Multiphase flowrate measurements play an important role during the reservoir characterization and production optimization phase of reservoir management. Accurate multiphase flow rate measurement is an indispensable tool for production optimization from oil and gas fields. One of the industry's accepted solutions is the use of multiphase flow meters, which are expensive, have a limited operational envelope, and are exposed to erosion and failures. This can limit the applicability of physical metering devices due to frequent calibration, transportation issues, space, safety, security, and possible high costs.

Virtual flow metering (VFM) is a method for estimating oil, gas and water flowrates produced from wells without measuring them directly. The method uses data available from the field, such as downhole pressure and temperature measurements as well as a choke position and ESP operational parameters, to estimate the flowrates by implementing hydrodynamic multiphase models, measurement data, and a reconciliation algorithm. In this paper, an overview of the conventional multiphase flow metering solutions is presented, which is followed by application of some advanced artificial intelligence and data analytics techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore.

The considered case refers to a typical scenario, where the measurements of oil, gas, and water flow rates are obtained in real time using a topside multiphase flow meter. Alternatively, the values of these multiphase rates are estimated using a data-driven dynamic flow model obtained using a dynamic mode decomposition technique. The results obtained with this method are compared with another VFM approach, where the rates are obtained using deep LSTM neural network.
Automated Tank Breathing
Authors: Sadid Metaab , Faraj Abdulla , Shateri Ahmed , Gopal Arun , Mahroof Dhasleem , Ahmed Niyas
Published: November 2020

Our Terminal Operation is fully automated wherein tanks and pumps are assigned by the system and human interface is marginal. In automated operation we need to segregate the tanks and pumps to avoid cross contamination. Blocked sections of pipe are exposed to thermal expansion and activation of Pressure Relief valves (RV). This resulted in the pipes exposed to pressure cycles and product being released by RV. System developed to address the issue.

Our Terminal operates on a start Stop mode. We identified the major Pipe sections which are responsible for the thermal expansion in the facility. These major Pipe sections were kept open to the same product tank at low level. This to ensure all thermal expansion in the pipe section are released to the tank. When the tank is designated for Breathing mode, then the tank is blocked by the system for any other operation to avoid the migration of undesired prodcucts to the tank. This is visually indicated in the screen to Alert the Operation Crew.

The entire terminal handled four major products. Pipe sections were grouped based on product type. Major Pipe sections were identified in each group. Frequency of the pipe section being locked were analyzed and a statistical model was evolved to identify the pipe section which required to be kept in breathing. Once the Pipe sections were identified then the possible destination for these pipe sections were identified. For each destination the path is identifiedto allow assets within this path to be recognized. The current state of the asset is ascertained, e.g. is it available for operation or subject tomaintenance. If the asset is subject to maintenance, then alternate path is explored. The path selection criteria are based on the minimum number of assets which should be in the selected path. Once the path is chosen the asset and the tank is blocked for breathing for that particular blocked pipe section.

In the event that the Pipe section, asset or Tank is required for Operation, then an alternate path is chosen for breathing. Any Pipe section that could be kept in breathing is alerted to the Terminal Operation crew, that at the earliest they need to keep the pipe section under breathing.

This proposed approach intends to replace previous practices where the thermal expansion of the hydrocarbon was released to the closed blow down system, which resulted in loss of finished product. The pipe section was exposed to cyclic pressure cycle, which increased the fatigue in the pipes and rendering the pipes operating close to their design pressure each time the relief valve was operated.

The simplicity and ease of operation is one of the key features offered by the proposed system. It also ensures the safety integrity of the Terminal assets.
An Innovative Carbonate Facies Modeling Workflow That Honors Geological Concept, Case Study of Thamama
Authors: Aidarbayev Sanat , Pamungkas Setiyo , Al Dayyani Taha , Lehmann Christoph , Ramos Luis
Published: November 2020

Objectives/Scope

One of the main uncertainties of reservoir modelling is understanding the reservoir properties in the inter-well region. Capturing variation in reservoir facies related to relative sea-level changes together with diagenetic overprint provides key information for the lateral reservoir heterogeneity. These observations can eventually be used as input for facies modelling to populate facies throughout the field. The conventional facies modelling approach defines lateral facies trends by using proportion data (pie-charts) for each reservoir zone. This approach has limitations to interpret lateral extension of individual facies or trends as the pie-charts show that all facies exist in the zone and everywhere throughout the field. However, incorporating the sequence stratigraphic concept into facies modelling workflow allowed us to draw discrete facies boundaries on sequence stratigraphic surfaces, which are of chronostratigraphic significance.

Methods, Procedures, Process

Integrated working sessions between sedimentologists and reservoir modellers lead to a new improved workflow in modelling reservoir facies. This case study provides a step-by-step workflow of the innovative facies modelling which is composed of three steps:

Create vertical proportion curve (VPC) to capture vertical 1D heterogeneity associated with higher order of sea-level cycles Construct pie-charts at important and critical sequence-stratigraphic surfaces, such as Sequence Boundaries and Maximum Flooding Surfaces. At this stage, a conceptual geological model is established that will serve to identify facies trends and heterogeneity in 2D. Defining probability maps to control the proportion of facies laterally to ensure facies ratios respect to each other.

Results, Observations, Conclusions

This workflow was applied to multiple stacked reservoirs of one of the UAE Onshore fields. Results started to show how the depositional environment controlled facies distribution over the field and helped to control reservoir properties of individual facies. With this workflow, the lateral facies predictability has increased. Sedimentological maps now help better to understand possible pathways for hydrocarbon flow from injector to producer wells. Incorporating higher resolution sequence stratigraphic cycles helped to understand the high-perm zones that are dynamically important to capture. The method was further tested in other multiple ADNOC Onshore fields where new geological facies maps also revealed reliable geological trends.
Methods and Concepts to Economically Improve Sour Gas Processing by Reducing the Complexity of the Plant
Authors: Özcubukcu Kadir Kagan , Jelinek Wolfgang , Finke Jan , Hahn Wolfgang
Published: November 2020

With depletion and natural decline of the sour gas fields in Germany, many conventional gas sweetening plants have been abandoned for economic reasons. A study and a concept selection were conducted to identify technical and economical competitive methods for a pilot plant, compared to existing conventional sweetening plants.

Several technical literature sources, patents and processes licensed by third parties have been researched. 58 methods were identified for sour gas sweetening, 8 methods for sulfur recovery and 9 methods for tail gas treatment. These were quantitatively ranked in a decision matrix. Plant concepts were developed, and basis of design were created with the technology owners for the three most promising processes. CAPEX was determined with the vendors input. OPEX were estimated using the basis of design.

Sour gas sweetening via amines is a reliable and cost-effective method. The focus of this paper is therefore on sulfur recovery and tail gas treatment. The tail gas treatment can be eliminated by highly efficient production of elemental sulfur, sulfuric acid or gypsum directly from the sour gas sweetening unit. This leads to halving of CAPEX.

Production of wet sulfuric acid via contact process using an innovative reactor is favorized. This modular plant can be integrated in existing sweetening plants (brownfield) or used for new installations (greenfield). Due to the fully automated process and high availability, OPEX can be significantly reduced.

This work uses a structured method to screen the available processes and to rank them according to the given requirements. The evaluation method can be used for other boundary conditions. Production of sulfuric acid directly from the sweetening unit could increase the lifespan of sour gas wells.
How Deep Graph Analysis Based on Piping & Instrumentation Diagrams Can Boost the Oil & Gas Industry
Authors: Bergmann Martin , Hertling Frank , Marquardt Sebastian
Published: November 2020

Most oil and gas plant operators would like to benefit from a virtual plant model, or digital twin, which allows them to access important data quickly. In reality, however, many have to work from large numbers of legacy analogue Piping & Instrumentation diagrams (P&ID) that have amassed over many years. Digitizing these to achieve a productive digital twin using traditional, manual methods can be an excessively expensive and time-consuming task.

A digital processing tool like PIDGraph is a much faster, more efficient approach. It treats all analogue P&IDs, irrespective of origin, as flat images which it can scan using artificial intelligence and convert into XML compatible files; these can be directly processed by most CAE tools.

Crucially, a solution like this can not only understand the blocks of plant equipment, but also the relationships between them as expressed by their interconnecting pipes and wires. This allows the user to build applications on top of the graph structure. Typical use cases are Equipment Isolation for maintenance requirements, and Module Engineering for engineering applications.

As time goes by and more of a plant's P&ID information is added to the system, it can serve as a data anchor for contextualization applications. The content stored can include not only P&IDs, but also maintenance reports, material specifications, manufacturer data and many other types of documentation. Users will be able to navigate quickly through vast amounts of data to find the points that matter in any given situation. They can use this information to improve plant safety, optimize operations, and reduce costs.
Polymer Dispersed Systems for Conformance Improvement in Fractured Carbonate Reservoirs
Authors: Matveev Sergei , Gazizov Aidar , Gazizov Almaz
Published: November 2020

One of the most important challenges faced by the oil industry is to enhance oil recovery from existing reservoirs, carbonate reservoirs are more difficult to develop than terrigenous reservoirs because of fractures and caverns, various lithologies, geological heterogeneity and complex, polymorphous pore space structure. All these factors limit the use of EOR methods that are normally applied to terrigenous reservoirs.

The main drawback in waterflooding carbonate reservoirs is premature water encroachment in production wells because of injection water breakthrough through the most permeable and productive layers and zones, often leaving entire formations and zones unswept or almost unswept while injected water preferentially flows through already swept zones and channels.

The essence of the PDS technology consists in alternating injections of a polymer solution and a dispersed suspension that form polymer dispersed systems in the reservoir to plug swept, water-saturated layers and channels with low flow resistance and thus produce hard-to-recover oil from less permeable zones by improving the sweep efficiency.

Pilot well treatment operations using a polymer dispersed system were carried out in injection well 5 of the Zabegalovskoe field. After the first PDS treatment, the oil production rate gradually increased by 120 B/D and the water cut in the area decreased by 14%. After the second PDS treatment, the oil production rate increased by 420 B/D and the water cut decreased by 16%.

The use of the PDS technology in the area for one year and one month resulted in an incremental oil production of 71,539 bbl and reduced water production by 39,948 bbl.
Seismic Facies Classification using Generative Topographic Mapping – A Case Study from Offshore Nova Scotia
Authors: Ray Amit Kumar , Dandapani Rajeshwaran , Verma Sumit
Published: November 2020

One of the major objectives of seismic interpretation is to effectively predict the distribution of reservoir facies away from well control. With the advent of increasing number of meaningful seismic attributes, it is time consuming and laborious to analyze them through conventional analytical methods. Machine learning techniques analyze higher dimensional data points faster and effectively. Automated seismic facies classification techniques are increasingly becoming important in identifying the potential hydrocarbon bearing zone and favorable facies. Such facies classification techniques, or, automated clustering algorithms, help arrange similar seismic traces based on the waveform shape, amplitude, phase, frequency, and other relevant seismic attributes. The main objective of automated facies classification using machine learning techniques is to perform facies classification fast and efficiently using several relevant seismic attributes for mapping the facies distribution and effective identification of the sweet spots.

The automated clustering algorithms fall into two categories – supervised and unsupervised algorithms. Unsupervised machine learning algorithms are purely data driven and help in recognizing and classifying the patterns from a dataset without any a priori information. A posteriori information such as well data, is integrated into the results for recognizing the facies classification and calibrating the interpretation. Unsupervised learning methods also help to highlight subtle stratigraphic features that might otherwise be unnoticed using conventional analytical methods.

In this study, we adopted a recent unsupervised classification technique called, generative topographic mapping (GTM). We applied this technique to a dataset from Offshore Nova Scotia, to extract the natural clusters from the seismic data for facies classification. Using the GTM technique applied to seismic data, we were able to map the distribution of different facies and potential sweet spots in the study area.
Modified Technique to Model Volatile Oil Reservoirs: Implications for Modern Software Programs
Authors: Ibrahim Mohamed , H. El-Banbi Ahmed
Published: November 2020

Volatile oil reservoirs are usually found in deeper geological formations. These reservoirs have become more important in recent years with advancement in drilling technology. Traditional material balance techniques used for performance prediction in black-oil reservoirs (e.g. Tarner's method described in Appendix-A) are not usually adequate to handle volatile oil reservoirs. A more appropriate technique is the one introduced by Walsh as in Appendix-B using the Modified black-oil (MBO) material balance equation. In this paper, a modification is introduced to expand Walsh technique applications. Both the traditional and the MBO prediction techniques were compared for many actual volatile oil fluid samples using compositional reservoir simulation. Comparison of the three methods revealed that the use of traditional techniques can introduce errors up to 35% when compared with results obtained from compositional reservoir simulation. The error increases with increasing volatility of the fluid and with less historical data available to perform prediction calculations. The Modified Walsh technique (presented in this paper) consistently produced results with less than 5% error when compared with compositional simulation results, however normal Walsh equation showed 10 % error. Tarner's and Modified Walsh's prediction methods produce close results for low solution gas and their results deviate significantly when volatility increases. Therefore, the Modified Walsh prediction method coupled with the generalized material balance can be used for any fluid type reservoir.

The three techniques were compared again for two actual field cases (one considered black-oil reservoir and another volatile oil reservoir). Both field cases were under depletion drive. Tarner's and Modified Walsh's prediction methods showed reasonable match with the actual performance for the black-oil reservoir, but only the Modified Walsh method could provide very good match with the actual field prediction for the volatile oil case. Using the traditional method for the volatile oil reservoir showed deviation of more than 35% in some parts of the historical data.
Flow Numerical Simulation of Tubing or Casing Leakage in Gas Well
Authors: Zhang Hong , Yuan Guangjie , Li Guotao , Li Jingcui , Wan Jifang , Fu Pan
Published: November 2020

Tubing or casing leakage of HTHP gas wells and underground gas storage wells affects wellbore integrity adversely. The wellbore micro-leakage detection technology can detect tiny fluid leakage in time and is of great significance to avoid malignant gas leakage accident. The theoretical basis of wellbore micro-leakage detection technology is characterizing the leakage flow field. To reveal the flow characteristics of gas leakage due to tubing and casing cracks and perforations, a CFD model of gas aperture leak has been established and flow field numerical simulation of high-speed and high-pressure gas outflow through aperture was conducted. The standard k-ɛ turbulence model was used to calculate and analyze the pressure and velocity distribution of the steady flow field. In addition, a time step was set on the steady-state model and the Scale-Adaptive Simulation (SAS) model was adopted to simulate the transient flow field with the initial conditions of the steady-state simulation results. The effects of the shape and size of the leakage hole, the pressure inside the pipe, and the medium in the outer annular space on the flow field were analyzed. The results show that obvious turbulence disturbance appears near the inlet of the leakage hole and supersonic under-expansion jet forms near the outlet of the leakage hole under the gas well working conditions, creating the expansion wave and shock wave. The flow velocity of gas leakage through the circular hole is the highest, while that of the slot hole is the lowest under the same conditions. The larger the leakage aperture is, the greater the pressure difference between inside and outside of the pipe is, and the more drastic variation of gas velocity in the leakage hole. The method and results provide theoretical guidance for the microleakage detection technology of gas well wellbore.
Inflow Control Devices ICD – A Historical Performance Analysis
Authors: Emegano Theodore Chinedu , Baloch Shahid Ali , Al. Alrefaai Mhd. Maher , Al Nuimi Saber Mubarak , Radwan El Sayeed
Published: November 2020

Inflow Control Devices (ICDs) and their variants autonomous inflow control devices (AICDs) have been deployed in horizontal wells in the onshore asset for more than thirteen years. The technology was adopted for their utility to manage: well inflows, sweep efficiencies, pressure drops along the well trajectory and delay fluid breakthroughs. Also, they are adapted to partially isolate fractures and or high permeability streaks which might introduce abnormal flow conditions in the production system-if unchecked. Horizontal wells were favourable target application, due to the notorious "heel-to-toe-effect" replete in horizontal wells, which ICDs are typically designed to remedy.

In the onshore asset, approximately fifty-one horizontal wells have been completed with ICDs or combination of ICDs-AICDs and some of them fully equipped for future open/close intervention capability using sliding side doors (SSDs). In this paper, a full assessment of ICDs design methodology and post installation performance over the 13-year period of ICD completions history is conducted. The performance evaluations entails ascertaining the extent to which the key design objectives are achived and overall value addition. Value based decision criteria for future ICD completion/ deployment is advocated. A proposal is submitted for further investingation into formulating universal or objective performance index (which will be independent of production data) for all well types. Such studies will include a single objective function capable of utilizing cross correlation values irrespective of well type to generate single KPI fuction.

Further recommendation (from experience) in the asset is that a do-nothing-after-completion scenario is not the best forward strategy. Greater value will be derived if ICDs completions have hindsight on ultramodern optimization post installation. Flexibility to optimize the wells in changing scenarios should be considered and built into the completion system from inception. Also the decision to adopt inflow control devices must simultaneously consider preperformance evaluation after installation. Hence data from offset wells, analougues or pre ICD performance of the study wells are input of utmost necessity for design and performance evaluation. Conclusively, a unified key performance indicator (KPI) for both ICD-completed producers and injectors is recommended for further investigation.
Performance Evaluation of Novel Perforation with Consistent Hole and Deep Penetration
Authors: Li Kai , Huang Penggang , Liu Yuan , Guo Hongzhi , Chang Cheng , Wang Bin , Li Yuanhua
Published: November 2020

Plug and perforation technique is the predominant method for multistage horizontal completion widely used in unconventional or tight reservoir applications. Perforation quality in such circumstances has a significant influence on completion efficiency. However, perforation performance is often described with an API RP 19B Section 1 test and not always analyzed thoroughly during the actual application. In this paper, we present several examples that qualitatively analyze perforation performance via operation data.

In the plug and perf method for multistage completions, the purpose of the perforation is to create a fluid entry point for hydraulic fracturing of each stage after the previous lateral section is isolated by plugs. In many fields, the initiation and extension of the fracture could be a critical issue and significantly slow down operations onsite. Theoretical analysis shows that perforation penetration and the effective hole number can lower the fracture pressure significantly, particularly in tectonic regions where stress anisotropy is at a high level. A novel perforation technique with consistent holes and deep penetration was tested and evaluated in several projects and it showed unique features applicable to reducing fracturing challenges. Three examples were reviewed to demonstrate that downhole perforation performance can be analyzed quantitatively through breakdown pressure comparison, pressure standard deviation analysis, and fracture extension hardness. This approach clearly showed that the novel perforation technique has advantages over traditional perforations.

Better perforations with consistent hole size and deep penetration are valuable for multistage completions in unconventional or tight reservoir applications. All the case studies in the paper show improved efficiency during multistage fracturing and completion execution. The analytical method introduced can also be easily adapted to evaluate and compare perforation quality in most multistage completion conditions.
Enhancing Reservoir Management by the Successful Drilling and Installation of a Dual Lateral Completion
Authors: Alshamsi Al Anoud , Yanez Hernandez Eglier , Zidan Maher , Al-Sereidi Amnah , Sanyal Subrata , Al-Harbi Ahmed , Mudgal Abhay , Beaman Daniel , Rondon Leonque , Butler Ben
Published: November 2020

The challenges of high heterogeneous multi-layer reservoirs were a driver for ADNOC to establish new well completion requirements. Based on this, the well completion strategy was revised in order to improve the completion design for a Dual Oil Producer (DOP) initially well planned without accessibility into the upper drain. The solution was successfully implemented by drilling the well having a Multilateral Dual String Level 5 Completion System to have full reservoir isolation and mechanical accessibility to TD in both drains.

A major approach was implemented giving the utmost importance to all operational risks concerning drilling and completion. The design relies on the Latch Coupling (LC) pre-installed with the production casing as the foundation for the successful drilling and cementing the upper drain liner and for different completion runs. Thus, a level 5 multi-lateral completion coupled with Inflow Control Devices and Pre-perforated Liner in the upper and lower lateral respectively was successfully deployed.

The main advantages of this technology compared to the initial proposal are:

Brings effectively two single wells from one slot of the wellhead tower as it provides independent access & control for enhanced reservoir management (well stimulation, production logging and water shut-off). Substantial cost savings due to the utilization of one slot only. Conventional single designs need two slots to deliver equivalent completion requirements. The completion technology meets the Well Integrity Policy & Completion Guidelines stated by ADNOC to have full accessibility to all drains.

This is the first Level 5 configuration installed in UAE integrated with a lower completion. The design enhances the benefits of the multilateral construction by providing full mechanical & pressure isolation at the junction throughout the life of the well. Furthermore, accessibility to both laterals from surface without the use of a re-entry deflector or any constraints was deemed to be paramount for a successful well construction.
Predictive Modelling of Drilling Fluid Rheology: Numerical, Analytical, Experimental and Statistical Studies of Marsh Funnel Flow
Authors: Ofoche Paul , Noynaert Samuel
Published: November 2020

This paper presents a two-parameter approach, using only density and Marsh funnel time for providing a complete rheological analysis of fluids, encompassing all six standard dial readings as established in the American Petroleum Institute (API) Recommended Practice 13D. The scope of the study includes experimental, analytical, numerical and statistical analyses of flow in the Marsh funnel. A mathematical analysis is developed based on the Marsh funnel’s geometry and a wide suite of machine learning algorithms are applied to model and predict rheological profiles across the funnel.

commercially available numerical simulation software is applied to build a model using computational fluid dynamics. Results from the numerical analysis are corroborated by analytical calculations that are then used to develop a statistical framework for predicting dial readings under various shear rates (3-600 RPM). Experimental results from more than 1500 mud tests are utilized to build ten machine learning algorithms modelling the rheological properties of the fluids. Their performances are evaluated to determine the best models based on three metrics: R-squared values, root mean square error and mean absolute error.

Predictions are performed on new mud data and comparisons are made among the ten predictive models broadly categorized into generalized regression models, decision tree-based techniques and miscellaneous approaches. The models show high predictive accuracies on new drilling fluid samples with the performances generally improving with increasing shear rates. A mathematical analysis of the geometry of the Marsh funnel has been utilized to establish a methodology to quickly and accurately perform rheological studies on fluids. Results from experimental, analytical, numerical and statistical studies all closely agree with each other.

The outcome of this study can readily be employed at the wellsite to obtain much value from the routine hourly Marsh funnel readings. This can serve as a quick substitute to infrequent conventional rheometer outputs, which are typically obtained only one to four times per day at the field. The rheological results and other derivates such as plastic viscosity (PV) and yield point (YP) are immediately produced after each Marsh funnel test. Informed decisions such as updating hydraulics modelling to improve ROP and hole cleaning can be thus be achieved. This work engages the disciplines of rheology and machine learning to produce a result that enhances the workflow during drilling operations.

Added value is derived from an already existing tool to better monitor fluid properties frequently, which is beneficial to the overall surveillance of the drilling fluid system.
Combining Augmented Intelligence and Edge Analytics for Improved Artificial Lift Systems Performance
Authors: Saghir Fahd , Gilabert Helenio , Boujonnier Matthieu , Bissuel-Beauvais Loryne , Boguslawski Bartosz
Published: November 2020

With the advent of IIoT enabled Edge Analytics, and its ability to run Machine Learning based inference at the extremities of a production network, it has become essential to enable Operators and Subject Matter Experts to transfer their knowledge to Edge Computing Devices.

This paper discusses the application of Edge Analytics enabled Augmented Intelligence for wells operated by Electric Submersible Pumps, where Machine Learning and Pattern Recognition Models help detect anomalous events in multivariate time-series data. These Models runs on Edge Computing Devices where identify newly discovered and well known ESP performance patterns that can be labelled by a Subject Matter Expert. Once these patterns are identified and tagged, the Models are retrained and pushed back to the Edge Computing Device, where they continue to detect and predict patterns in real-time.
Models Cross-Validation as a Best Practice for Optimization of Well Stimulation
Authors: Alexander Bochkarev , Dmitriy Chernyshev , Narkes Gaysina , Andrey Lisitsyn
Published: November 2020

Development of low-permeable oil reservoirs with complex bedding often requires application of production stimulation methods such as hydraulic fracturing.

Success of well stimulation greatly depends on quality of geomechanical and hydraulic fracture models. Several different workflows can be used to build a certain model when the input data available are necessary and sufficient. However, in the absence of some of the key data, it is necessary to use rational assumptions instead, but doing so automatically regards model output as probabilistic.

This paper concentrates on steps necessary to constrain initial assumptions made within models and demonstrates the result obtained. We present an integrated workflow used for cross-validation of geomechanical and hydraulic fracturing models based on production history match as a constrain.

Relatively thin 35 deg API oil-bearing ∼ 0.3 mD hydrostatically pressurized zone, represented by interbedding of heterogeneous sedimentary layers of gross height of ∼ 15 m out of which about 5.5 m is pay, with argillitic barriers at top and bottom (see Fig. 2) has active water-bearing formation 45-50 meters above. Production history available for stimulated horizontal wells has shown a significantly increased water-cut compare to one expected from the zone of interest.

Earlier frac company stress models were made by tying pressure gradients - "regional experience" - to gamma ray and SP logs and attributing E, KIc to lithology. These fracture models were consistently showing hydraulic fracture containment by barriers of the target zone and, thus, fracturing into water zone was not viewed as a highly probable cause of an increased water cut.

Our target was to resolve the water cut problem by means of geomechanical and retrospective hydraulic fracturing modeling. These two models were initially built on incomplete data due to the absence of:

pore pressure measurements mechanical properties (static Young's modulus) from core analysis.

The models were then cross validated by constraining on production history while utilizing Young's modulus as variable. PEBI-grid based reservoir simulator is fully coupled with finite difference planar full-3D hydraulic fracturing simulator and allows direct transfer of fracture geometry (conductivity) for multiphase flow simulation.
Chemical Sand Consolidation Design in Jasmine Field: Lessons Learned and Critical Success Factors from an Operator Point of View
Authors: Dachanuwattana Silpakorn , Prasongtham Pattarapong , Kulchanyavivat Sawin , Chigbo Ikenna , Sreeroch Asadawut , Grassian David
Published: November 2020

The Jasmine oil field, in the Gulf of Thailand, has several pools of unconsolidated sandstone reservoirs. Multi-zone completion is usually deployed in these reservoirs, however, sand control in this completion type could be a challenge. Several sand control techniques were evaluated and the most promising strategy was found to be chemical sand consolidation treatment. In the design phase, the most critical challenge was to determine the optimal and safe chemical recipe and placement technique.

A single chemical recipe optimized for all of the reservoirs was achieved, yielding an unconfined compressive strength (UCS) of at least 500 psi while retaining a permeability of 40%. Bullheading was selected as the placement technique. To minimize contamination of the chemical with annulus fluid, we designed to shorten the distance between the packers of each completion zone. Cut-to-release packers were used instead of pull-to-release packers to mitigate the concern of unwittingly unsettling the packers. A risk assessment was conducted to cover all aspects of the job. High risk activity like pumping flammable fluids at high pressure was specifically singled out and special mitigation was put in place to ensure a safe execution of the activity. Some other mitigations such as minimizing nearby hot surface areas, real-time monitoring of oxygen levels, and purging the mixing system with CO2 were also considered.

This paper provides a case study for chemical sand consolidation treatment from the view of an oil company and covers the full lifecycle of the application from concept selection to the finalized procedure, taking into account both technical and operational considerations. This research contributes to the public body of knowledge within the oil and gas industry with regards to an emerging technique for sand consolidation.
Breakthrough Formulation of Novel Modified HF Acid System: Realizing Remarkable Value in Sandstone Reservoirs
Authors: Mohd Jeffry Suzanna Juyanty , M Shafian Siti Rohaida , M Hatta Siti Aishah , Madon Bahrom , Mohamad Khairul Idzwan , Adlan Ezamy
Published: November 2020

Matrix acidizing is one of the most common well stimulation methods in Malaysian brown fields. Formation damage caused by fines migration has been reported to be among the main reason for productivity decline. The need to conduct acid stimulation arises when the initial rates of the wells are below expectation or when the productivity of the reservoir drops significantly. Various mud acid formulations have been used to treat the wells from the past until recent years. However, some well treatments resulted in low success rate especially for reservoirs with vast variation and complex formation mineralogy. This highlights the need for modified acid recipe from the conventional mud acid system. Mud acid systems are often highly corrosive, resulted in unwanted secondary precipitation and require multiple additives during the treatment.

This paper highlights the evaluation and comparison of the novel Modified HF acid against conventional mud acid system through core flooding tests. Multiple cores with various mineralogy were tested. The laboratory test showed that the Modified HF acid system able to generate better permeability improvement compared to mud acid systems. The modified acid recipe gradually sequestrates the major iron minerals throughout the injection to avoid unwanted secondary or tertiary precipitation that could damage the formation. Subsequently, the chemical was successfully piloted in two fields and replicated in other fields across Malaysia. The field applications proved that the novel Modified HF acid system generated outstanding results in production gain and improved the total economics of the project. The novel acid recipe can be an alternative solution when simpler acid systems are favored with lower treatment cost. This chemical is also beneficial for wells that located in small platforms or jackets where minimal amount footprint is available during job execution.
Methodology to Derive Gas Oil Ratio and Other Formation Evaluation Applications of Downhole Ultrasonic Sound Speed Formation Tester Measurements
Authors: Torlov Vladislav S. , Amin Asif , El Sharaky Mohammed
Published: November 2020

The paper presents a methodology and results of a study to derive the gas-oil ratio (GOR) of oil in situ based on downhole ultrasonic sound speed formation tester measurements. Authors provide several cases as a reference for in-situ fluid acoustic measurement values, and cleanup profiles in various borehole and downhole settings, to illustrate their benefits for formation testing as well as other geoscience applications.

Formation tester dynamic fluid measurements of analysis of 47 samples from 25 wells were assessed. Reference GOR measurements were collected from different sources. The relationship between in-situ sound speeds corresponding to reference GOR was assessed and showed strong correlation within the investigated range. To enhance estimation accuracy, another two methods of deriving GOR using fluid slowness, downhole condition parameters, and computed pseudo-density are presented.

The assessment of formation tester fluid slowness data showed its consistent association with live oil GOR within the studied range (~20-1,500 scf/bbl). Further increase in GOR showed change in the relationship trend. Several methodologies of GOR estimation are discussed including multiple linear regression using fluid slowness, in-situ pressure, temperature, and linear regression with estimated pseudo-density of a fluid. The multiple regression output showed slightly better match with laboratory data. Results of other data modelling approaches are also discussed.

The proposed GOR evaluation methodology is based on acoustic surveys made downhole without phase separation. The results are available at the time of formation testing cleanup and fluid sample capturing stages. The additional benefit of the method is to aid in assuring fluids are maintained in single phase for collection of representative samples.
Machine-Learning Distributed-Temperature-Sensing-Based Pipeline Leak Detection
Authors: Sfar Zaoui Wissem , Lauber Thomas , Pohl Clemens , Kerk Michael , Glaeser Thomas , Jelinek Wolfgang
Published: November 2020

In this paper, we present results of field trials using a novel machine learning (ML) pipeline leak detection algorithm based on the distributed temperature sensing (DTS) technology. The installation is based on a buried, 9km-long pipeline in Germany. The transported medium is wet oil with a temperature varying in the range of ~20°C–35°C. A multimode fiber optical cable (FOC) is laid at ~10 cm distance from the pipeline and is connected to a Raman-based DTS system. For verification, the leak test setup is composed of a water tank, heater, pump, and an injection pipe introduced on top of the pipeline at an unknown location for the DTS system, thus performing blind testing. The water was heated to 33°C (TEST1) and 19°C (TEST2) to simulate the temperature range of the transported medium and injected at a release rate of 36 L/h (TEST1) and 20 L/h (TEST2). The DTS system was adjusted to automatically alarm when the evaluated temperature deviation reaches 3°C with 3 confirmations. The ML alarm evaluation method was able to detect the anomaly at the test position after only 0.8 barrel (TEST1) and 1.4 barrel (TEST2) of total released fluid volume. In comparison, classical gradient-based approaches would require up to 5 times higher alarm threshold, which may not be reached due to the low temperature difference of the oil to the ground.
Assessing the Feasibility of Deep Eutectic Solvents For CO2 Capture From Molecular And Process Modeling
Authors: Alkhatib Ismail , Alba Carlos , Bahamon Daniel , Llovell Felix , Vega Lourdes
Published: November 2020

The soft-SAFT equation of state is employed for the screening of deep eutectic solvents (DES) for CO2 capture based on key performance indicators, namely, CO2 absorption capacity, and enthalpy of absorption. This work belongs to a long-term project on assessing the adequacy of advanced EoSs (soft-SAFT) as a tool for the evaluation and design of alternative solvents for CO2 capture.

The models for the pure DESs and their mixtures with CO2 are validated with experimental data, and subsequently used in a fully predictive manner to calculate the thermophysical properties needed for evaluating solvent performance. Results demonstrate that under the same operating conditions, [TBA][Cl]:LA (1:2) is the most promising DES in terms of amount of CO2 absorbed and enthalpy of absorption. However, the comparison of these solvents to 30 wt.% aqueous MDEA solution shows that the promise of DESs is far from adequate in terms of CO2 solubility, being 13 times lower than that obtained with aqueous MDEA. Nevertheless, DESs show a clear advantage in terms of lower enthalpy of absorption, being 3 times less than aqueous MDEA, along with the absence of water as a co-solvent. The collection of the acquired results confirms the reliability of the soft-SAFT EoS as an attractive and valuable screening tool for CO2 capture and process modelling.
Multi-Stage Frac Sleeves Completion Risk Matrix - A Quantitative Approach to Minimize Stuck Incidents while Deploying Completions Across Horizontal Slim-Holes
Authors: Vega Roberto , Simmons Yvan , Shakeel Muzzammil
Published: November 2020

Drilling and completing wells for hydrocarbon production comprise the major cost of well operations. Slim-hole drilling can significantly reduce drilling costs, making low producing reservoirs economically viable. However, a number of challenges are associated with slim-hole wells. A major challenge encountered while deploying lower completions in long horizontal slim-hole wells is stuck pipe, as the wellbore diameter is less than six inches, allowing restricted clearance between the wellbore and completion equipment. Stuck pipe incidents can cause significant non-productive time (NPT), additional high costs, and the possibility of compromising the well objective.

Stuck pipe incidents may require sidetrack operations in the worst cases, which may considerably change the planned well design and delay the overall project, along with increasing costs. Drilling in the minimum stress direction with heavy mud weight and high overbalance pressure adds to the challenge in deploying completions in HPHT slim-hole wells. While deploying the lower completions in slim-hole wells, many factors come into play and each factor has a critical role in the success of the operation.

After conducting a detailed study on several HPHT slim-hole gas wells in Middle-East region, a risk matrix was developed for a specific well-bore diameter, by identifying some common variables. Based on potential chance of occurrence, a certain value was assigned to each variable. The risk mitigation plans are driven by the risk matrix variables. Using this risk matrix, the occurrences of stuck pipe incidents in critical HPHT slim-hole wells that initially tend to have high chances of stuck pipe were significantly reduced.

The risk matrix presented in this paper successfully mitigated stuck pipe incidents from occurring and has saved millions of dollars in associated costs. The matrix played a key role in operator's drilling operations and was adapted in operations as an important tool to evaluate final wellbore conditions prior to selection of the optimum completion type. This paper presents a highly effective risk matrix that significantly minimizes stuck pipe incidents during drilling/completion operations of slim-hole gas wells.