bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of Artificial Intelligence in the Petroleum Industry: Volume Loss Prediction for Naturally Fractured Formations
Authors: Al-Hameedi Abo Taleb , Alkinani Husam H. , Dunn-Norman Shari , Al-Alwani Mustafa A. , Alkhamis Mohammed M. , Al-Bazzaz Waleed H. 
Published: October 2019

Abstract
Recently, artificial intelligence has gain popularity in the drilling industry since more wells are drilled in hostile environments. One of the most difficult problems have been encountering the drilling operation is the problem of lost circulation. The complexity of the lost circulation problem is due to the interaction between the parameters that are causing this issue. The aim of this work is to create artificial intelligence models to predict lost circulation, equivalent circulation density (ECD), and rate of pentation (ROP) prior to drilling for naturally fractured formations.
Lost circulation events from 500 wells were collected and analyzed to comprehend the impact of each drilling parameter on lost circulation. The data were cleaned and outliers were removed. Partial least square (PLS), a supervised machine learning algorithm, was utilized to create three models to estimate mud losses, ECD, and ROP before drilling. The models went through a cross-validation process to validate them. In addition, the models were tested with new data that were not used in the process of creating the models.
The results showed that the three models can predict mud losses, ECD, and ROP within a reasonable margin of error. Testing the models with new data of 30 wells drilled showed that the models' predictions closely track the actual values from the real data. Moreover, the new models were compared with previously developed models for naturally fractured formations. The new models showed better predictions for the actual values than the previously developed models, suggesting the ability of the new models to predict mud losses, ECD, and ROP within an acceptable error. In addition, a 10% sensitivity analysis was conducted for all models to quantify and understand the effect of each parameter on every model. Mud weight (MW) had the highest impact on the ECD and mud losses models revealing that in order to minimize mud losses and ECD, the first action should be trying to use as low MW as possible. On the other hand, weight on bit (WOB) showed the highest positive influence on the ROP model and total flow area (TFA) of the nozzles showed the highest negative impact on the ROP model. Thus, the models developed in this study can be used to regulate the drilling parameters to minimize mud losses.
The methodology used in this study to develop estimation models for mud losses, ECD, and ROP can be applied to create predictive models in other formations if the required data are available.
Using Computer Simulation to Overcome Unforeseen Challenges during Start-Up and Commissioning of a Water Injection System
Authors: Addicott Glyn 
Published: October 2019

Abstract
This paper will discuss a past Hydraulic Analysis Limited (HAL) project and the challenges of commissioning a Water Injection (WI) system. The paper will demonstrate how the use of an accurate pipeline simulator which models the full control system can reduce both commissioning time and risk by identifying issues early. The simulator can then be used to modify and tune the control systems prior to and during commissioning.
A computer simulator of a Water Injection system on a Floating Production Storage and Offloading (FPSO) vessel was constructed, including all pipework, pumps and control systems. The simulator was initially used for a design based hydraulic and surge analysis study. During commissioning, the simulator was updated with as-built information and was then used as a commissioning support tool to tune and optimise the various control systems prior to commissioning. All commissioning tests were simulated and assessed before being carried out so that potential issues could be identified and resolved using the simulator.
The simulator identified a number of unforeseen issues associated with the start-up of the hydraulic systems. If these issues had not been identified early then there would have been significant delays to the project. The number of hours expended during commissioning was significantly lower than expected with little ‘fire-fighting’ of problems. The simulator was used on a daily basis to check every commissioning activity via a morning web conference meeting and screen share to run through the simulation results and show the expected pressure and flow readings for that days tests. The expected pressure and flow readings could then be compared to the actual readings during the commissioning tests, ensuring a high level of confidence. By comparing the simulator output to the actual site pressure and flow readings, any unexpected system response was rapidly identified and the system was safely shutdown prior to unacceptable operating conditions occurring. The cause of the problem was then identified and resolved through modifications to the FPSO control systems.
A Physic-Based Scaling Curve of Gas Production Under Compaction: A Leading Way to Better Machine-Learning Prediction
Authors: Sajjad Farasdaq , Chandra Steven , Naja Savinatun , Suganda Wingky 
Published: October 2019

Abstract
We present a simple analytical solution to diagnose gas production under compaction. This solution scales production profile of different wells and collapses them into a single general curve. The curve will later serve as the "learning" function for physic-based machine-learning prediction.
A rapid growing flood of big data in the oil and gas industry reveals a substantial opportunity to the better understanding of hydrocarbon reservoir. With machine learning, one can turn a numerous amount of data to predict future production and determine field economics. However, the quality of the prediction from machine learning is dependent on the learning function selected that most of the time does not concatenate any physical aspects of the problem. In this paper, we offer a better machine learning with a physics-based function to estimate future gas production under severe compaction.
We construct a physic-based master curve by solving the coupled Darcy-Biot equation for vertical gas well under reservoir compaction. We assume that the flow is radial and the porosity is transiently changing by the reduction in pore pressure due to gas production. Finally, we reduce the complexity of the coupled non-linear equation to two scaling optimization parameters: a mass scaling factor to scale the recovery factor and time scaling factor to scale the diffusion time.
We verify our model with a field case from KLX field, Indonesia. This gas field produces an enormous amount of gas with subsidence as the side effect. The subsidence was identified by knowing the change in platforms level. By collapsing the production profile of all existing wells into a single master curve, we capture the universal scaling parameters that represent the behavior of gas flow under reservoir compaction. Furthermore, we can substitute the resulted master curve as the learning function for to the machine-learning model to predict and diagnose other fields in the future that undergo the same phenomena.
We find that reservoir compaction leads to a higher recovery factor of gas for a long term. However, the high subsidence rate is not a favorable condition for the offshore field as the production facilities on the platform will submerge under sea level in a matter of years. Thus, the field owners must consider some subsidence mitigations such as injection and maintaining critical production rate.
Our novelty is to produce a general scaling to describe gas production under compaction, which is later useful for the development of our machine-learning process to simplify the prediction process, not involving extensive and expensive numerical simulation.
A Scalable Parallel In-Situ Combustion Reservoir Simulator for Large Scale Models
Authors: He Ruijian , Wu Shuhong , Chen Zhangxin , Yang Bo , Liu Hui , Shen Lihua 
Published: October 2019

Abstract
As a competitive recovery method for heavy oil, In-Situ Combustion (ISC) shows its great potential accompanied by technological advances in recent years. Reservoir simulation plays an indispensable role in the prediction of the implementation of ISC projects. With the computational complexity, it is imperative to develop an effective and robust parallel in-situ combustion simulator. In this paper, a mathematical model for ISC is proposed, which takes full consideration of ISC related physical phenomena, including multi-dimensional multi-component three-phase flow, heat convection and conduction, chemical reactions, and mass transfer between phases. In the mathematical model, different governing equations and constraints are involved, forming a complicated PDE (partial differential equation) system. For physical and chemical behaviors, some special treatments for the ISC simulator are discussed and applied. Also, a modified PER (Pseudo-Equilibrium Ratio) method is proposed in this paper. A fully implicit scheme is applied, and discretization is implemented with the FDM(Finite DifferenceMethod). In solving nonlinear systems, the Newton Method is introduced, and both numerical and analytical Jacobian matrices are applied. Due to the complexity of an ISC problem, an appropriate decoupling method should be considered for which the Gauss-Jordan transformation is developed. Then, with certain preconditioners and iterative solvers, a numerical solution can be obtained. The results of different models are presented, which are validated with the results by CMG STARS. Also, the scalability of parallelization is shown, indicating the excellent performance of parallel computing. This accurate, efficient and parallel ISC simulator applies to complex reservoir models.
Harnessing the Power of Natural Language Processing and Fuzzy Theory to Improve Oil and Gas Data Management Efficiency
Authors: Asfoor Hasan , Kaskas Walid 
Published: October 2019

Abstract
Recent years have witnessed advancements in the oil and gas (O&G) industry related to the 4th Industrial Revolution and advanced analytics. O&G data has been a key that fuels these advancements. Proper management of O&G data could unleash the full potential of analytics for the O&G industry. Tackling this challenge may involve time consuming manual steps that may introduce inaccuracies along the way. In this work we aim to improve the efficiency of O&G data management in three aspects. First, by simplifying the tagging process of O&G files and largely reducing the manual work involved. Second, by enhancing the searchability of O&G files. Third, by reducing the overhead of data redundancy.
Our approach utilizes Natural Language Processing (NLP) techniques and Fuzzy theory concepts to produce three elements. The first element is a metadata extractor, which can automatically extract search keywords from O&G files. The second element is an algorithm we built to tag O&G files. This algorithm can be trained against predefined business categories and label a target file with the right category. It can also produce a (fuzzy) degree of membership of the file to other categories. This algorithm can be used to automate tagging of legacy historical files. The third element is a utility that we built to enable data managers find similar and duplicated files across multiple O&G file repositories. It also combines the first two elements to simplify and automate tagging of historical, untagged, files and migrate them to a central repository. Our approach and algorithms do not use any metadata defined manually by users. Rather, they work entirely on textual content inside files to tag them, discover similarities and extract their metadata.
We tested our approach on an O&G dataset of text documents related to drilling, exploration and production. This dataset comes in different formats (reports, spreadsheets and presentations) and is distributed over multiple repositories. The test included two scenarios: document tagging and discovering duplicated files. The results show that our tagging algorithm was able to achieve over 95% tagging accuracy. Automated file tagging with such accuracy can significantly reduce the steps to classify O&G files. This can be useful when migrating legacy data to a central repository. The test also resulted in discovering all duplicated files with a similarity score between 95% and 100%. This enables data managers to migrate the correct version of duplicated files to a central repository and thus achieve data accuracy, reduce the search effort, and also reduce disk space required.
Our work introduces a unique approach to optimizing the efficiency of O&G data management and automate key steps such as document tagging. In addition, we believe that the tagging algorithm we built is unique in the O&G industry.
Liquid-Bead Solution for Lightweight Cement Slurries
Authors: Chan Lakmun , Vasquez Jorge , Flores Leon Carlos 
Published: October 2019

Abstract
Design, execution, and evaluation of cement operations using a new liquid-bead cement system helped achieve the necessary top of cement for development wells in Brunei. Glass beads are a common lightweight material used within the oil industry to generate low-density slurry designs with competent compressive strength development. For Brunei offshore operations, using dry beads poses many challenges, such as rig silo management, quality control of blends, and waste. A new approach has been proposed using a liquid-bead system to produce lightweight cement slurries by adding beads stabilized within a fluid as another liquid additive to help reduce the need for dry bulk blending of beads. Planning, execution, and post-operation evaluation methods were developed for this new approach.
The liquid beads can be added to cement slurries using liquid additive pumps. For Brunei's operation, a mobile injection skid was developed to inject liquid beads at a predetermined rate into the suction header of the cement unit triplex pump while the base slurry is being mixed and pumped at a predetermined rate on the fly. Extensive laboratory tests were performed on the liquid slurry and the final set product to demonstrate the liquid-bead influence on cement properties compared to a dry-blend bead design. To validate this technology, a field mixing trial was performed at the rigsite. Cement evaluation with conventional logging tools poses an additional challenge for beaded slurries. To evaluate cement placement, a bond log post-process analysis was performed.
The first Brunei offshore trial operation, executed during April 2018, was a 9 5/8-in. production casing where 13 m3 of a liquid-bead cement slurry design was mixed, pumped, and successfully placed within the annulus. Laboratory testing validations and injection skid and post-operation cement evaluations are discussed.
Interrelationships of Rock Elastics Properties to Petrophysical Pore Structure and Geometric Details of Sandstone
Authors: Akbar Muhammad Nur , Salam Agihtias , Awab Malik , Prakoso Suryo 
Published: October 2019

Abstract
This paper presents new interrelationships of elastic properties of the porous rock to petrophysical parameters in order to enhance better understanding on how compressional and shear waves velocities, young modulus, bulk modulus, lame’s coefficients, and Poisson’s ratio correlate with permeability, porosity, petrophysical geometric details of the pore system, and microscopic geological features.
This study employs 215 sandstone core-plugs from North West Java Basin. The approach used utilizes the Kozeny-Carman equation with Re-arrangement made on this equation leads to rock typing based on pore structure similarity, similar in pore shape factor and tortuosity. By plotting pore geometry against pore structure variable, it shows that P- and S-wave velocities, young modulus, bulk modulus, Poisson’s ratio, and lame’s coefficients data can be clearly grouped based on rock type and have strong correlations with permeability, pore geometry, and pore structure parameters. This grouping method enables to investigate the main influential factors that systematically control rock elastic properties. The interrelationships of each parameter of P-wave and S-wave velocities, young modulus, bulk modulus, Poisson’s ratio, and lame’s coefficients versus pore geometry and pore structure were constructed. The critical finding is that each relation among the rock groups of each elastic property is clearly separated. Each rock type has a similar pattern in that P-wave and S-wave velocities increase with pore geometry and pore structure variables. These velocities tend to be high with an increase in Kozeny constant. However, for a given porosity for all the groups, these velocities increase remarkably with a decrease in Kozeny constant. In other word, velocities increase with either an increase in the complexity of pore systems or, at the same pore complexity, a decrease in specific internal surface area. In addition, young modulus, bulk modulus, and lame’s coefficients also increase with pore geometry and pore structure in each rock type.
As a novelty, this study helps to characterize the elastic properties based on porosity and permeability data in the form of pore geometry and pore structure. Furthermore, this work can help us to predict some elastic properties at in-situ condition as long as we have the porosity and permeability values interval.
Novel Time Saving Well Integrity Assurance Methodology
Authors: Stein Arild 
Published: October 2019

Abstract

Objectives/Scope
To enable and verify an installed barrier in any well with wireline intervention only. Removing the need for large pressure testing volumes or equipment to test installed barriers from surface. Especially when there potentially are structural issues in well barrier envelope that can limit the pressure test, inefficiencies or weak spots in the subsea structure or Xmas tree / well barrier envelope.
The wireline toolstring includes a setting tool with a monitoring system to log the setting signature of the plug.
A verification plug installed above the barrier with a verification module alters the pressure/forces below and sends the measured values to the setting tool above. The bespoke product setup provides a verified, documented barrier. Case history from a pilot job included to illustrate the principle.

Methods, Procedures, Process
The plug installation depth in the well is a compromise between requirements and verified structural integrity of the tubing, especially if installing a bridge plug as the primary barrier. The setting forces, anchoring and sealing signature of the plug is monitored and recorded. This individual signature will be verified against a library of ISO approved barrier installation profiles. Anomalies can be identified and trigger the need for a decision to release and relocate the plug. A verification plug may be installed near the primary plug. The verification plug installation signature will be surveyed in the same way as the primary plug to determine its proper installation. Verification is then achieved using the verification module between the installed plugs and logged by the setting tool above.

Results, Observations, Conclusions
Pressure manipulation in the volume between two installed plugs will cause a load in the direction of flow on the lower primary plug and a (an industry standard) test from above by the verification plug. Upon completion, this operation produces the necessary documentation to leave the plug(s) as an approved barrier in the wellbore. Equipment is re-usable after re-setting at surface.

Novel/Additional Information
By documenting this sequence, we now have a lower plug verified as a barrier and can remove the verification plug above or leave it as a secondary tested and verified barrier if needed. All performed without needing to use large volumes of test medium from surface and waiting for thermal adaptation and fluid segregating to get a stable test readout at surface.
A New Method to Predict and Assessment the Risk of Shallow Water Flow in Deep-Water Drilling
Authors: Wang Huanhuan , Yang Jin , Hong Jiayao , Zhang Dongyufu , Meng Lingyu , Xu Jiajun , Chen Lei 
Published: October 2019

Abstract
Shallow water flow is one of the geological disasters faced by deep water drilling operations. The common way to predict shallow water flow is applying qualitative prediction of seismic profiles and the accuracy is low. This paper proposes a new approach based on simulation experiment to quantitative predict the shallow water flow in deep water.
Based on the characteristics of propagation velocity of acoustic wave in deepwater and rock media, the simulation experiments of acoustic wave characteristics of shallow water flow have been conducted. The influence of shallow water flow in seabed soil on the propagation velocity of acoustic wave in rock and soil is revealed. The simulation experiments also show the relationship between the pressure and porosity changes in shallow water formation and the propagation velocity of acoustic wave. As a result, a template for predicting and identifying shallow water flow by acoustic wave is established, and the risk grade of shallow water flow to drilling is evaluated.
The results show that the greater the detected acoustic velocity, the greater the shallow water flow pressure in the formation and the greater the risk of drilling. The pressure and porosity of shallow water flow stratum are in a power exponential function relationship with the acoustic wave velocity. According to the site application results, the accuracy of this prediction model is more than 80%, meet the requirement of deepwater drilling design and site construction.