bluuebunny/crossref_metadata_2025 huggingface dataset:
Curing Losses: Lab Developments and Best Practices
Authors: Al-Yami Abdullah , Wagle Vikrant , Al-Anqari Khawlah , Aljohar Abdulwahab , Elkatatny Salah
Published: February 2022

Moderate to severe loss circulation problem encountered while drilling is a major economic burden that increases the total well construction cost dramatically in some cases. This study will focus on best practices to mitigate curing losses in terms of prevention of potential loss by pre-treatment, locating and identifying the losses, identify the mechanism of losses and severity, curing losses strategies and their best drilling practices. In addition, the study will detail typical curing losses materials and their limitations. Finally, selected examples of recent developments in curing losses materials are detailed. The examples are activated nanoparticles dispersed fluid, Epoxy polymer resins, and sodium acid soluble cement solutions.

Pre-treatment of loss circulation zone with proper size of bridging materials can reduce and eliminate mud losses into porous formations. The choice of bridging agents will depend on the formation characteristics. The moment of the occurrence (while drilling, circulating or tripping), the severity of the loss (seeping, partial or complete) and the exposed formations are the key factors to identify the best remedy for loss circulation. Losses to reservoir zones are handled differently than non-reservoir zones, wherein only acid soluble or non-damaging material is used in the pay-zone. Seepage losses occur in any formation type when the particle size distribution of the bridging agents is not optimum enough to create an effective seal. This paper describes three different types of novel loss circulation materials to control moderate to severe losses.

The paper describes the development of a novel nanoparticle-based chemical treatment composition for its potential use as a loss circulation material. The gelation times were evaluated at different temperatures up to 300°F. The effects of pH and different concentration of activators on gelation times of the new treatment fluid has also been studied. Second example of LCM described in this paper is a novel resin composition comprising an epoxy resin and chemical activator to treat moderate to severe losses. The effect of the chemical activator concentration on the setting time of the resin composition was investigated to accomplish a controlled and delayed polymerization. The third example of LCM described in this paper involves a formulation consisting of an acid soluble cement and a sodium silicate gel that has a high potential to control moderate to severe loss circulation problem. The objective of this acid soluble loss circulation system technology was to design a solution to cure lost circulation in reservoir zones. This is the first study to discuss and detail such topics related to curing losses.
Quantitative Research on Development Status of Heterogeneous Reservoirs in Offshore Oilfields
Authors: Guan Cuo , Zhang Jian , Li Xianjie
Published: February 2022

Multi-layered reservoirs have unbalanced production levels due to the contradictions between layers in offshore oilfields. At present, there is a lack of rapid and effective methods to quantitatively analyze and evaluate the development status. In this paper, based on the B-L theory and the equivalent seepage resistance method in the water drive process, a relationship chart between the average saturation of a single layer and the seepage resistance between injection and production wells is established. Using the test results of single well pressure and production well profile, single layer seepage resistance can be obtained, combined with saturation-seepage resistance chart to obtain single layer average water saturation, and then the single layer recovery degree at the time of testing can be determined. In order to establish a Lorentz curve model describing the relationship between the cumulative single layer recovery ratio (or fluid absorption index) and the ratio of formation coefficient, the recovery degree (or liquid absorption index) of each single layer and its corresponding formation coefficient are counted. Based on that Lorentz curve model, it can effectively quantify and characterize the production equilibrium degree of multi-layer reservoirs. After using this method to backcalculate the development status of a certain well group in the Bohai Oilfield, the characteristics of the degree of production and the development of large channels in each layer can be obtained. it also quantifies that the imbalance index used by the well group has risen from 1.2 in 2014 to 2.1 in 2019. This paper proposes a new method for analyzing reservoir development dynamic characteristics, which can more objectively and truly evaluate reservoir development characteristics and quantitatively evaluate the effects of adjustment measures, and provide efficient and fast technical support for the efficient development of offshore fields.
GPU-Enabled Scalable Multiscale Solver for Reservoir Simulation
Authors: Manea Abdulrahman
Published: February 2022

As reservoir simulation models continue to grow in their size and complexity, the computational cost of reservoir simulation is constantly increasing. Since most of the reservoir simulation time is typically spent in the linear solver (being in the innermost part and the most challenging to parallelize and scale), efficient linear solvers are of utmost importance for reducing reservoir simulation turnaround times. In this work, we study the scalability of a versatile multiscale linear solver, namely the restriction-smoothed basis multiscale method (MsRSB) (Møyner and Lie (2016)) on the emerging massively parallel GPU architecture, and compare it to its performance on the multi-core CPU architecture.

MsRSB, unlike traditional multiscale approaches, uses iterative smoothing to adaptively compute multiscale basis functions, allowing it to handle a wide range of difficult grid orientations seen in real-world industrial applications. While MsRSB can be parallelized directly, its reliance on a smoother to determine the basis of functions results in unusual control and data flow patterns. To achieve effective scalability, these patterns must be carefully designed and implemented on massively parallel systems. We extend Manea et al. (2016) and Manea and Almani (2019) work on parallel multiscale methods to move the MsRSB special kernels to shared-memory parallel multi-core and GPU architectures.

Highly heterogeneous multimillion-cell 3D problems, adopted from the SPE10 Benchmark (Christie and Blunt (2001) have been used to illustrate the scalability of our parallel MsRSB development. The GPU implementation is benchmarked on a massively parallel architecture consisting of Nvidia Volta V100 GPUs, while the multi-core implementation is benchmarked on a shared memory multi-core architecture consisting of two packages of Intel's Haswell-EP Xeon(R) CPU E5-2667. For both the setup and solution stages, we compare the multi-core implementation versus the GPU implementation. The GPU-based MsRSB implementation shows great scalability, with over a 4-fold reduction in runtime when compared to the optimized multi-core implementation.
A Rapid Multi-Objective Intelligent Decision Method for Shale Gas Fracturing Parameters Based on Machine Learning: A Case Study in the Changning Reservoir
Authors: Wang Lian , Yao Yuedong , Wang Kongjie , Adenutsi Caspar Daniel , Zhao Guoxiang , Dong Peng
Published: February 2022

Proper design of fracturing parameters does not only increase the productivity and profitability of shale gas wells but also reduce the damage fracturing fluids cause to fluid-sensitive formations. Unfortunately, fracturing parameters design methods developed so far rely heavily on simulation runs and experience of field engineers. To lessen the computational burden and decrease the risks during fracturing, this study proposes a simple-yet-rigorous rapid intelligent multi-objective decision (RIMOD) method for shale gas fracturing parameters. The proposed RIMOD method includes two key sections, the first one is the squares support vector regression (LSSVR) model for objective evaluation and the other is non-dominated sorting genetic algorithm-II (NSGA-II) for finding the best shale gas fracturing parameters. Meanwhile, the flowback ratio of fracturing fluid (FBR) and first-month gas production (PROD) were considered as the objectives while fracturing parameters such as number of fractured sections, proppant amount and fractured length were selected as the decision variables. According to the data from sixty-six fractured horizontal wells from the Changning shale gas reservoirs, the intelligence decision process was completed in less than ten seconds. Furthermore, the accuracy of optimal scenarios in the final Pareto front was tested with the numerical model includes the geological features of the Changning reservoir. It was found that for both FBR and PROD the mean relative errors were less than 4%. It was concluded that, compared with simulation and empirical methods, the proposed RIMOD method could obtain fracturing parameters with greater efficiency and reliable precision. A field engineer could choose a scenario from the final optimal solution set based on the demand of the company. This could be reduction in formation damage or increased single well production. More importantly, once the data of a fractured well is obtained, this approach can be used to immediately establish an intelligent decision model for designing the fracture parameters of newly drilled wells.
Novel Methods for Simulating Reservoirs with Tilted Fluid Contacts
Authors: Dow Eric , Kashinath Abishek , Jayasinghe Savithru , Middya Usuf , Dogru Ali
Published: February 2022

This paper introduces two new approaches for modeling reservoirs with tilted fluid contacts. We first introduce a method based on local capillary pressure adjustments, which uses adjustments of the capillary pressure near the oil-water contact to ensure that the contact surface does not move during production. The second approach uses hydrodynamic aquifer flow to support the oil-water tilt. A non-linear inverse problem is solved to determine the parameters that control the aquifer flow. Both approaches are implemented in a parallel reservoir simulator and applied to a synthetic reservoir case.
Integration of Engineering & Geomechanics Lead to a Successful Sidetrack, Drilling, and Completion
Authors: Al Dhamen Ali , Aleidhi Abdulaziz , Vossen Johannes H , Chakrabarti Prajit
Published: February 2022

A landing section of a well in newly developed area of a field had three challenges: the well trajectory cuts through shale intervals at high inclination; the well plan was limited to a certain trajectory window where weak bedding planes failure could not be ruled out by changing the plan; the previous section's casing was short landed due to wellbore instability challenges. An effort to initiate a sidetrack twice was not successful due to wellbore collapse, therefore a new sidetrack depth had to be identified. Geomechanics group collaborated with Drilling Engineering, and Drilling fluid services to successfully initiate the sidetrack, drill the well and land the liner casing safely to bottom.

A geomechanics model was built based on wireline logging data from offset wells. First, stress azimuth was constrained from oriented multi arm caliper of multiple wells around the field. Then, rock properties were derived from acoustic logging data, and the model was calibrated against drilling events and caliper enlargements. Finally, Geomechanical and petrophysical parameters were utilized to design fluids parameters and additives. A starting mud weight window was defined, and a sidetrack point was selected in strong rock interval. The well was monitored in real time where Logging while drilling, acoustic caliper, and azimuthal density image were utilized for breakouts onset monitoring. Furthermore, drilling parameters were monitored for hole cleaning and mud weight control.

The stress azimuth around the field demonstrates a rotation of 15° from the north toward the east which introduced uncertainty in defining MW in the well trajectory that could be affected by rotation. The sidetrack was successfully initiated as expected, the hole was drilled to bottom, and liner landed successfully. However, a significant increase of MW from its starting value was required, and the model was subsequently adjusted based on LWD data. Maintenance of high concentration of bridging while continuously nourishing the mud helped in stabilizing the wellbore. Unlike previous sidetracks, non-productive time was eliminated with significant cost reduction.
Well Integrity Challenges and Latest Responses to Abnormally High-Pressured Formations in Deep Gas Wells
Authors: Corona Mauricio , Franco Nata , Veloso de Oliveira Adriano , Moretti Fabricio , Alismail Ahmad Ali
Published: February 2022

Drilling through the deep and tight dolomitic interval is challenging due to significant pore pressure anomalies across a giant gas field in the Middle East, resulting in an increase of the drilling fluid density that on some occasions has exceeded the 20 ppg value.

Extensive offset well analysis and observed patterns in the sub-surface attributes can support the identification of abnormal high-pressure zones and selection of suitable well design. The sub-surface model is fed with the occurrence of these high geo-pressures and the attributes are re-processed to continue mapping the risk, seismic response versus actual high-pressure events.

Different studies have been performed in several years in attempt to understand and predict the behavior of the high-pressure zones. Due to field heterogonous stratigraphic and areal distribution, that correlation was not always able to identify them, and still drilling risks such as well control, downhole loss circulation and stuck pipe significantly impacting operations. Under well dynamic conditions, keeping the mud stable at high densities is just one of the important aspects in drilling and cementing operations, which small mistakes can compromise well integrity and, in worst-case scenarios, result in abandoning the well for not being able to reach the main reservoir target.

This paper describes the major operational and well integrity challenges faced during the well construction and latest innovative responses to safely handle abnormal and unexpected high-pressure zones. Mitigation measures in terms of well control, drilling and cementing fluids, drilling practices and contingency well design to counteract the challenges will be described in length in the paper.
Correcting Classified Activities with Natural Language Processing
Authors: AbouLaban Said , AlAwami Nassar , Herve Philippe , Amrite Jaidev
Published: February 2022

For Drilling Contractors and Oil & Gas operators, daily drilling activity reporting is crucial to understand the underlying efficiency of the drilling process, identify risks and uncertainties at the well level and field level, look for opportunities to improve average rate of penetration and to be within the target drilling budget and target time. All the previous while increasing the safety of the people and assets and have the lowest impact in the environment.

Daily drilling reports help drilling contractors and operators to understand what, where and when an activity was performed, helps managers to understand teams and contractors performance, and helps executives have a holistic view of the drilling process, and how it meets predefined Key Performance Indicator targets. Analysis does not only look into visible non-productive time, but extends into invisible non productive time. Examples of visible non productive time include drilling borehole assembly failure, critical rig equipment failure, etc. Examples of non visible non productive time include premature pulling out of hole of drilling equipment assuming high bit wear, variations between crew experience during drilling resulting in lower rate of penetration, etc.

The challenge the industry faces is that daily drilling activity reporting; along with real time reporting, provides the foundation for drilling analytics. And inaccuracies in reporting the drilling activity, such as reporting the wrong activity, would lead to inaccurate analytics and insights. To overcome this challenge, operators standardize drilling activities reporting by defining drilling codes and subcodes that are used by Drillers, Tool Pushers and Company men to map a drilling activity to a usable code and subcode, and in turn, those codes and subcodes can be used to extract structured data out of the drilling reports for reporting purposes.
A Data Driven Method to Predict and Restore Missing Well Head Flow Pressure
Authors: Huang Ruijie , Wei Chenji , Li Baozhu , Xiong Lihui , Yang Jian , Wu Suwei , Gao Yan , Liu Shuangshuang , Zhang Chenjun , Lou Yuankeli , Li Zhengzhong
Published: February 2022

With the rapid development of oilfield exploration and development technology and the continuous improvement of automation and informationization, the petroleum industry has entered the era of digitalization and intelligence. Surveillance data including pressure and saturation play increasingly role of reservoir development machine learning modelling. A large amount of high-quality data can enhance the accuracy and robustness of the neural network model. However, during actual reservoir development, not all data are easily available. Data acquisition is limited to high-cost tests techniques, pressure bulid-up test, reservoir saturation test, and production logging test etc. In this study, we develop a novel workflow to forecast and restore missing well head flow pressure (WHFP) by utilizing Random Forest (RF) algorithms based on the actual dataset obtained from the middle east carbonate reservoir. To evaluate the effect of prediction pressure results, we employed predicted WHFP into production prediction neural network model. With the support of predicted WHFP, the quality and accuracy of neural network production prediction model is much improved. This study provides a low-cost and high-precision WHFP prediction and restoration method for reservoir engineers in decision making.
A Systematic Approach to Leak-Off Pressure Prediction in Unconventional Resources –A Case Study from Junggar Basin, China
Authors: Jiang Zhibin , Sima Liqiang , Zhou Wei , Wang Xiaoguang , Xu Hanbing , Ding Kun , Qi Lisha , Zheng Sheng , Xie Dan , Lian Liming , Qian Chuanchuan , Wang Jie , Bai Zhiwen
Published: February 2022

Mud loss is one of the most severe incidents encountered while drilling. As the focus of exploration has moved to the deeper and more complicated formations, mud loss happens more frequently, causing safety incidents and lowering the drilling efficiency. The lost mud invades into formations, makes harm to the reservoir and affects the accuracy of formation evaluation. Large numbers of studies have proved that the key strategy to deal with mud loss is to prevent it and follow with remedial action. The prerequisite of such measurements is locating the formation of mud loss. Loss pressure is the highest pressure that formation can hold before mud loss happens. It is important for deciding mud weight and critical for fractured formations. Therefore, an innovative ring-cracking loss model is created by integrating minimum horizontal principal stress loss model, statistical loss model, and critical crack width loss model after analyzing the loss mechanism of fractured formation. These four models are applied to the fractured formation of block X, and the results are compared to the actual downhole pressure.The result from ring-cracking-based loss model is less than the actual downhole pressure. This model is suitable for field application and can guide the design of mud density.
A Data Driven Machine Learning Approach to Predict the Nuclear Magnetic Resonance Porosity of the Carbonate Reservoir
Authors: Ayyaz Mustafa Ayyaz , Zeeshan Tariq Zeeshan , Mohamed Mahmoud Mohamed , Abdulraheem Abdulazeez
Published: February 2022

Carbonate rocks have a very complex pore system due to the presence of interparticle and intra-particle porosities. This makes the acquisition and analysis of the petrophysical data, and the characterization of carbonate rocks a big challenge. Neutron porosity log and sonic porosity logs are usually considered as less accurate compared to the NMR porosity. Neutron-density porosity depends on parameters related to rock matrix which cause the inaccurate estimation of the porosity in special cases suchlike dolomitized and fractured zone. Whereas NMR porosity is based on the amount of hydrogen nuclei in the pore spaces and is independent of the rock minerals and is related to the pore spaces only.

In this study, different machine learning algorithms are used to predict the Nuclear Magnetic Resonance (NMR) porosity. Conventional well logs such as Gamma ray, neutron porosity, deep and shallow resistivity logs, sonic traveltime, and photoelectric logs were used as an input parameter while NMR porosity log was set as an output parameter. More than 3500 data points were collected from several wells drilled in a giant carbonate reservoir of the middle eastern oil reservoir. Extensive data exploratory techniques were used to perform the data quality checks and remove the outliers and extreme values. Machine learning techniques such as random forest, deep neural networks, functional networks, and adaptive decision trees were explored and trained. The tuning of hyper parameters was performed using grid search and evolutionary algorithms approach. To optimize further the results of machine learning models, k-fold cross validation criterion was used. The evaluation of machine learning models was assessed by average absolute percentage error (AAPE), root mean square error (RMSE), and coefficient of correlation (R).

The results showed that deep neural network performed better than the other investigated machine learning techniques based on lowest errors and highest R. The results showed that the proposed model predicted the NMR porosity with an accuracy of 94% when related to the actual values. In this study in addition to the development of optimized DNN model, an explicit empirical correlation is also extracted from the optimized model. The validation of the proposed model was performed by testing the model on other wells, the data of other wells were not used in the training.

This work clearly shows that computer-based machine learning techniques can determine NMR porosity with a high precision and the developed correlation works extremely well in prediction mode.
Local Manufacturing of Oilfield Chemicals- Mitigating Potential Future Disruptions While Supporting Local Content Program
Authors: Al-Abdullatif Abeer , Khan Aman Ullah , Ibrahim Awaji Ayoub , Alhussain Abdulhakim , Garwan Ahmed
Published: February 2022

Halliburton uses the term "local content" to describe programs to develop and use local resources in providing our services in that host country. Local content requirements vary by country, but commonly include providing employment opportunities to local citizens, procurement of goods and services from within the country, manufacturing and value addition through partnerships with and development of local entities, training programs to develop the technical skills of local individuals and businesses, and carry out research and development for finding indigenous alternates of imported materials. In almost all cases, local content requirements are mandated by the laws of the countries where we operate.

Adhering to and improving local content is an important part of Halliburton's commitment to support the countries in which it operates and it also brings benefits for both Halliburton and local communities. In this paper, we share a case study of how Halliburton carried out the process of localizing chemical manufacturing in Saudi Arabia, the steps taken, and support extended in developing the local suppliers. Meeting local content requirements requires precise collaboration and communication between regional and country management teams, compliance with host country laws and regulations, adherence to Halliburton company policies, and meeting the quality standards the National Operating Company which is the end user in most cases. The methodology for achieving effective localization results started with setting the right strategy and putting challenging but achievable targets.

This localization initiative exemplifies company efforts to create value in every aspect of the company's business through the In-Kingdom Total Value Add (iktva) program mandated by the national operating company in the country.

Having a local source of manufacturing and supply chain mitigates any disruptions like the one we saw during COVID 19 whereby the movement across borders was partially closed and supply chain globally was disrupted. Any local souring effectively diminishes the impact of any such disruptions.

This initiative considered more than 50 Halliburton commercial chemical products and resulted in more than 10 successful replacements. Halliburton was able to export three products to company's operations outside Saudi Arabia. Partnering with Saudi Aramco, chamber of commerce and local manufacturers and suppliers in this program will drive additional domestic value creation to support a rapidly changing economic environment and foster future prosperity.
Out of Distribution Detection of Well Logs for AI-Assisted Formation Evaluation
Authors: Stuov Nikolay , Zaytsev Alexey , Ismailova Leyla , Kovalev Dmitry , Katterbauer Klemens , Marsala Alberto
Published: February 2022

In formation evaluation, we assess and predict the structure of rock formations and saturating hydrocarbon distribution by comparing and determining the similarity between different well properties. For similar wells, in a specific field and geological context, there is an expectation that the subsurface reservoir properties around the wells are similar or share similar characteristics.

In many cases, it is more important to look not directly at similarity, but at how anomalous a well or a well interval is. We develop a data-based model for novelty and outlier detection procedures for oil&gas well intervals. The model constructs scores for measuring a similarity between wells using well logs data. Then, it uses these scores to identify anomalous wells and well intervals. Technical, our approach consists of two steps. In the first step, we construct a representation of well intervals using a self-supervised autoregressive approach. In the second step, we calculate similarity scores for outlier detection based on these representations. The representation of the complete well interval is a mixture of the representations for the various intervals.

The results demonstrate the strong performance of the proposed framework. We evaluated different machine learning models in terms of their performance and analyzed their estimates in a self-supervised learning problem on predicting well log measurement ranges. For out-of-distribution detection based on multiple wells log data, deep neural network-based methods provide significantly better performance than simpler models, such as linear regression. Analysis of the neural network algorithms shows that they can represent the well logs whose embeddings adequately represent the wells.

The developed similarity learning framework presents a first and innovative approach to determine the specific distribution for subsurface reservoir properties. It will help petrophysicists with well logs correlation and interpretation and serve as a recommendation engine for assisted formation evaluation.
Application of Xgboost Algorithm in Rate of Penetration Prediction with Accuracy
Authors: Zhou Fei , Fan Honghai , Liu Yuhan , Ye Yuguang , Diao Haoyu , Wang Zhifa , Rached Rached , Tu Yulin , Davio Eduardo
Published: February 2022

In oil and gas exploration, rate of penetration (ROP) is one of the most important parameters that affect drilling costs and drilling performance. Therefore, prediction of rate of penetration (ROP) with accuracy has become an important direction of current drilling work. In order to solve the problem that the existing models are not accurate enough to predict the ROP, this paper proposes an ensemble learning approach called extreme gradient boosting (XGBoost) model.

In this paper, in addition to the XGBoost model, different models were constructed through various approaches, such as traditional theoretical/empirical models (Hareland model and Motahhari model) and machine learning models (support vector regression (SVR), artificial neural network (ANN) and k-nearest neighbor (KNN)). Then, the accuracy of the constructed models was compared with each other. Aiming at the problem of ROP prediction, the historical data of a specific field that has been collected is mined. The input data for training these models includes mud logging parameters and geomechanical parameters. Before data is entered into the model, preprocessing is required. After normalizing the input data, ategorical variables, and use One-hot encoding method for converting categorical variables into a form which can be used in machine learning models. These data were used as input variables, and ROP is the output variable for modeling. Useing the GridSearchCV tool in scikit-learn to perform grid search and cross-validation to find the optimal parameters of the model.

Comparison of accuracy and computational performance between above models for rate of penetration prediction, the results show that the XGBoost model with prediction accuracy of 85%, the accuracy of other models is 48% (Hareland model), 45% (Motahhari model),51% (KNN), 52% (ANN)and SVR (55%), respectively. It is obviously that XGBoost model has higher accuracy than traditional theoretical/empirical methods and other machine learning models, indicating that XGBoost model has stronger ability to deal with high-dimensional and non-linear problems. XGBoost model can be used to predict the ROP at the specific field reliably and accurately provided the model be developed with enough data, which can provide a scientific and reliable reference for reducing drilling costs and improving drilling performance.
The Contribution of Virtual Reality in Awareness and Preparedness of Oil and Gas Professionals
Authors: Kokkinos Nikolaos , Mitropoulos Athanassios
Published: February 2022

Human factor played a dominant role in the majority of the past technological accidents. Ignorance, carelessness or even worse negligence by operators were only some reasons of those accidents. Chemical industry keens on new techniques for optimizing human and environmental safety. The main scope of this study is to examine the contribution of virtual reality in awareness and preparedness of oil and gas industry professionals in emergencies. The methodology based on an in-situ threefold assessment process of the chemical industry staff on virtual reality high tech facilities at the Department of Chemistry of International Hellenic University. Firstly, the control room operations of the examinees were assessed in a state of the art Virtual Control Room (VCR) environment. Then, participants’ interaction with faults, incidents and accidents was examined in a Virtual Reality Oilfield (VRO) using wearable technology. Finally, examinees’ skills in crisis management were evaluated in a Virtual Dispatch Center (VDC). Various emergency what-if scenarios were investigated which were not hypothetical or fiction products, but rather the result of long-term interviews with industry executives, field operators and control room operators in order to reproduce accurately, efficiently and effectively near-misses, dangerous incidents and accidents. Useful results were drawn regarding the behavior of the participants during emergencies, their teamwork performance and their interaction with the virtual system.
Digital Twin of Expensive Multiphase Flow Loop Test to Develop Next Generation of Production Technologies
Authors: Karimi Muhammad Akram , Arsalan Muhammad , Shamim Atif
Published: February 2022

Multiphase flow is frequently encountered in upstream O&G industry that has significant impact on the development of numerous production technologies such as multiphase flowmeter. Before the deployment of these technologies in an oil/gas field, the technologies are tested in a multiphase industrial flow loop test that emulates multiphase test conditions. This paper presents a digital twin of 2-phase flow (oil & water) as a low cost alternative to expensive multiphase flow test.

We have adopted backward strategy to design the digital twin of multiphase flow. At first, we characterized our proprietary microwave water-cut (WC) meter in an industrial flow loop in variable test conditions. Then, multiple digital models of the flow regimes were built and tested on our microwave WC meter. One of those models (rotated zigzag) was able to accurately predict WC sensor response over full WC range in oil continuous as well as water continuous flow conditions under varying salinity levels.

Two sets of responses have been recorded and compared – first obtained from the industrial flow loop trials and second from our EM simulation model. Key microwave resonator parameters such as resonant frequency (f0) and quality (Q) factor have been compared under varying conditions. The comparison suggests that f0 & Q-factor give higher sensitivity against WC in oil continuous and water continuous flow conditions respectively. Moreover, WC sensor performance was also compared under varying salinity conditions in the range of 20,000 ppm to 80,000 ppm and digital twin is able to successfully predict the sensor response in these conditions as well.

Significant amount of resources are spent on setting desired flow condition such as flow regime, WC and required salinity level. Our proposed digital twin model is able to emulate all of these multiphase flow conditions at negligible cost. It can help develop & test new production technologies without requiring to spend huge amount of money on lengthy, complex and expensive multiphase flow loop tests.
Segmentation of X-Ray Images of Rocks Using Supervoxels Over-Segmentation
Authors: Alqahtani Hussain , Alqahtani Naif , Armstrong Ryan T. , Mostaghimi Peyman
Published: February 2022

Digital core analysis has gained the interest of many scientific communities because of its impact on our understanding of flow in porous media. A typical workflow in digital core analysis includes scanning, reconstruction, denoising, segmentation, and modeling. Image analysis and modeling highly depend on the quality of the segmentation step. In this regard, conventional image segmentation methods often require user input/interference. This results in user bias and may produce a range of possible segmentation outcomes. To address this, we propose an unsupervised machine learning framework that offers multiple functionalities including improved mineral and micro-porosity identification. Superpixel (2D) and (3D) work by over-segmenting greyscale images using a family of over-segmentation algorithms. Simple Linear Iterative Clustering (SLIC) is one of these algorithms that is recognized for its speed and memory efficiency. The proposed framework utilizes SLIC and unsupervised clustering methods for segmenting greyscale images. SLIC divides the 2D and 3D images into segments having pixels (or voxels) with similar features (i.e., intensity range). Statistical features of each segment are computed and used for identifying the segment label through unsupervised clustering techniques. The unsupervised voting clustering implements a majority voting policy from multiple clustering algorithms including Hierarchical clustering and k-means clustering. A North Sea sandstone 2D X-ray image along with its SEM image were used to validate this framework. Different metrics were used to measure the accuracy of the X-ray segmentation with SEM segmentation. Our results show a mean Jaccard index of 70% and a mean Dice index of 81%. The same workflow is applied using supervoxels on a high-resolution 3D Indiana Limestone image and the results show similar accuracy margins compared to watershed segmentation. Comparison with other segmentation methods shows an average Jaccard score of 74% and an average Dice index score of 83%. To the best of our knowledge, this is the first application of superpixels over-segmentation algorithms in semantic segmentation of X-ray micro-CT images of porous media. The findings of this study highlighted the advantage of these algorithms in detecting sub-resolution porosity regions in greyscale images and obtaining accurate multi-label segmentation.
Utilizing Immersive Technology to Enhance Plant Operations and Speed Up Knowledge Transfer
Authors: Balhareth Hamad
Published: February 2022

The objective of this paper is to share current operational challenges concerning Oil and Gas (O&G) operators, and to suggest certain solutions/applications from the IR4.0 technology toolbox. Augmented Reality/Virtual Reality (AR/VR) will be discussed to address the operational challenges detailed in this paper, while simultaneously showing how AR/VR can be leveraged to speed up knowledge transfer process, especially at the current alarming rates of attritions.

The proposed approach is to first provide clear and concise explanation of the current challenges from an O&G operational point of view. Next, AR/VR will be explained and clearly defined to show its general benefits, before zooming in to innovate, and capitalize on specific features to address the challenges at hand. AR/VR can play a key role in managing other installed technologies (i.e. artificial intelligence, mobility, etc.) to transfer essential data to proper personnel at the right time and location. In other words, AR/VR can link up to all other applications and create one central hub to organize, streamline, and create actionable data in any facility. While showing the operational benefits, AR/VR will also be discussed to show how it can speed up knowledge transfer and reduce training costs, especially when software developers work together with O&G technologists to create meaningful product, rather than a generic one that truly serves no one.

The result is showing that AR/VR can clearly solve and enhance O&G operations by delivering important prescriptive messages with knowledge-based actions to concerned parties at the proper location, so they can take timely, proactive actions. Simultaneously, acting ahead of time translates into reduced plant upsets, while increasing safety and well-being of all plant personnel.

Most O&G companies employ stat-of-the-art technologies to address challenges affecting various parts of their operating facilities. Integrating these technologies and allowing operators to organize and strategize where and how to start, and showing potential links is itself a challenge. AR/VR can address this predicament, if employed properly. This paper will address AR/VR implicit and explicit capabilities in more details.

The novelty of this paper is not in presenting AR/VR technology, as it has been around for several years, but rather in showing how to design an agile system to take advantage of the technology. The newly designed system can incorporate several scattered software applications across a facility; therefore AR/VR will act as an orchestrator by managing the flow of data and ensuring they reach the right personnel at the right time.
Halliburton Saudi Arabia Crisis Management and Lessons Learn During COVID-19 Pandemic in the Kingdom of Saudi Arabia
Authors: Albish Roa'a , Al-Qarni Fahad , Al-Zaidy Khalid
Published: February 2022

During the first quarter of 2020, the world encountered a crucial and unprecedented health crisis. The global transmission of COVID-19 poses a significant challenging situation for Oil and Gas industry, particularly in the absence of standardized procedures and recognized methods. Like many other countries worldwide, Saudi Arabia implemented the lockdown for utmost public and private services and controlled population movement through curfew. With the execution of these tight mitigation requirements, Halliburton Saudi Arabia has been able to maintain business continuity by looking at the basic approach of health, safety, and environmental (HSE) processes through crisis management decision making and utilizing digital solutions.

The purpose of this paper is to showcase how Halliburton Saudi Arabia developed sustainable adjustable process and methods that reduced exposure and the pandemic-related potential risks associated with working in offices, rig sites, workshops, and laboratories while maintaining business continuity in operation, manufacturing, and technology.

Halliburton Saudi Arabia preformed risk analysis, tracking systems, exposure modification methodologies, communication strategies and management decisions that helped the company overcome challenges during the pandemic.

The implementation of risk assessments, adaptable safety procedures and utilizing more than 5 digital platforms, served Halliburton employees and its work force throughout 2021 and into 2032. In this paper, we share lessons learned during the pandemic, how we overcame the unprecedented health crisis and how we continue to deal with the pandemic impact.
Securing Business Processes Using Blockchain Technology: A Case Study of Hydrocarbon Accounting Processes.
Authors: Odachi Kenechukwu
Published: February 2022

This research addresses the problems associated with hydrocarbon accounting reconciliation and allocation from the production facility to the export terminal. This paper further discusses the security of the hydrocarbon accounting database and the overall automation of the production value chain, providing transparency to the joint venture partners involved in the crude oil export agreement to avoid revenue loss. It also provides a system that is not prone to malware or data alteration and promotes hydrocarbon allocation among the injectors, production data management and production data security.

The existing system mostly in the sub-saharan Africa lacks trust and greater transparency. A new technology will be devolped using Blockchain Hyperledger which is a distributed ledger technology. For the purpose of this research Javascript, PHP, MySQL on Apache virtual machine was used to design and simulate the blockchain network. The web interface of the system was tested using katalon web test framework using Agile methodology. The system will have to integrate with the Lease Automation Custody Transfer (LACT) and Supervisory Control and Data Acquisition (SCADA), A LACT-SCADANode synchronized system will be implemented in the custody transfer point in the export pipeline, so all the oil and gas Exploration and Production (E&Ps) companies injecting crude oil can view what each company in the network injected and also the quantity that got to the export terminal daily.

This will be achieved by having all the custody transfer nodes of the E&Ps and export terminal node in a network of consensus. Production data will be distributed at strategic points during the transportation among the nodes uniformly at the same time, making it impossible for cyber-invasion on all the nodes at the same time. The outcome of this research will achieve an advanced secured transparent system to store production data for accurate hydrocarbon allocation, in which the consensus attribute of the implemented blockchain technology will give each node autonomy. Hence, making the production data highly reliable and reconciliation will be achieved at real-time.

This innovation further presents a new knowledge in the application of mechatronics engineering to the oil and gas sector, with its multidisciplinary focus on electronics, mechanical and computer systems. It is the integration of a software system to a LACT and sensors on the crude oil pipelines to acquire data, compute in a Hyperledger fabric network and display at real-time for hydrocarbon allocation settlement
Data Science Adoption and Operationalization in the O&G Industry: Challenges and Solutions
Authors: Zouch Mariem
Published: February 2022

The Oil and Gas (O&G) industry is used to cycles of lows and highs due to different challenging economic and political situations. Yet the challenges caused by the sanitary crisis due to the covid-19 pandemic are certainly like no others. The shutdown of a large number of social activities had a direct impact on energy consumption. Many studies [1], [2] and [3] have been published at the beginning of the covid-19 pandemic to predict impacts of the restrictions imposed on a global scale: decline in global oil demand, saturation of storage capacities and delay of exploration and production projects. Companies facing this unprecedented crisis had no option but to adopt innovative ways of driving costs lower and maximizing operational efficiency. As a consequence, the pace at which Data Science (DS) is finding its way to O&G applications has been noticeably accelerated although the O&G industry is one of the latecomers to digitalization [4]. The adoption of DS and data-driven solutions has moved from gaining acceptance in the industry to becoming a necessity to many companies. According to a Gartner survey [5], the O&G industry commitment to investment in digital transformation in general had become the first priority in 2021 while it was third-highest priority in 2019 and not even funded in 2014. This involves investments in data acquisition techniques through innovative sensing technologies but also investments in advanced data aggregation and analytics platforms. AI/ML/analytics are listed in the same survey [5] as "top game-changing technologies in 2021". The 2021 survey also states that 50% of the O&G companies have plans to increase their investments in AI/ML and related fields such as cloud-computing.

But adoption and operationalization of DS does not come with no challenges. Acceptance and reliance on data-driven models need a favorable cultural and technical environment that is not necessarily compatible with the conventional corporate-like outlook of O&G companies: Data privacy and ownership regulations can diminish DS efforts. Security restrictions can prevent deployment of ML models to end users. All of these challenges are accentuated by the absence of a clear process model to implement and manage DS projects. In this paper, we survey the actual challenges the O&G industry is facing and present a number of corresponding solutions. The paper is structured as follows. The first section explores the state of the art of data-driven models in the O&G industry. The second section lists the challenges DS is facing within the O&G industry and proposes a classification of these challenges into three main classes, namely: human, data and infrastructure related challenges. The paper also proposes an O&G specific framework for DS projects to overcome these identified challenges.