bluuebunny/crossref_metadata_2025_split huggingface dataset:
A First Attempt to Predict Reservoir Porosity from Advanced Mud Gas Data
Authors: Anifowose Fatai , Mezghani Mokhles , Badawood Saleh , Ismail Javed 
Published: February 2022

Abstract
Porosity, a critical property of petroleum reservoirs, is a key controlling factor of the reservoir storage capacity. It has been conventionally measured from core plugs. Empirical correlations, statistical, and machine learning methods have been employed for indirect estimation of porosity. The results obtained from these approaches are available only after acquiring drilling and wireline logs. Obtaining porosity estimates in real time, ahead of wireline logging, can help in making critical decisions and enabling early assessment of reservoir quality. We present the results of a machine learning approach to predicting porosity from advanced mud gas data. Datasets integrating advanced mud gas data with porosity were gathered from seven wells to prove this concept. The mud gas data includes light and heavy flare gas components. Optimized artificial neural network (ANN) models were applied to the datasets and multivariate linear regression (MLR) models were used as benchmarks. Each well dataset was split into training and validation subsets using a randomized sampling approach with the ratio of 70:30. A 100 ppm cut-off was applied to the total normalized gas. To evaluate the performance of the models, we use correlation coefficients (R) and mean squared error (MSE). The ANN models consistently outperformed the MLR models in all the datasets. The ANN models have training and validation correlation coefficients of up to 0.89 and 0.88, respectively, compared to an average of 0.79 and 0.77 for the MLR models. The training and validation MSEs for the ANN models are as low as 0.0135 and 0.021, respectively, compared to those of the MLR models in the range of 0.0007 and 0.03, respectively. These results indicate the nonlinearity of the relationship between porosity and the gas components. Furthermore, it can be deduced that the approach is feasible and better results are achievable. The randomized sampling ensures that each data point has an equal chance to be used for either training or validation without bias. The cut-off applied to the normalized total gas is a standard practice to eliminate the background gas effect in the mud gas data. This study provides an opportunity to utilize mud gas data beyond the traditional fluid typing and petrophysical correlation purposes. The presented approach has the capability to complement existing reservoir characterization approaches in providing reservoir quality assessments at the early stage of exploration. We plan to apply state-of-the-art machine learning models and perform sensitivity analysis on the gas components in the future to increase the accuracy.
Operations Sustainability During COVID-19 Pandemic
Authors: Maitieg Mohamed , Rachmat Febry , Baseef Hussam , Al Hadyani Fahad 
Published: February 2022

Abstract
The paper aims to describe the experiences, challenges faced, and lessons learnt on how The Company Well Services division is managing the risks associate to COVID-19.
The global COVID-19 pandemic spread in late 2019 and turned into one of the biggest challenges facing all industries including the oil and gas industry– in decades. The most challenging aspect of COVID-19 is its contagion rate, and its ability to spread from both non-symptomatic and symptomatic people to others who are in close contact through respiratory droplets, by direct contact with infected persons, or by contact with contaminated objects and surfaces.
Today, we must consider this new reality into business continuity planning and take all necessary precautions to mitigate its spread within the organization. As the health and safety of the employees and contractors is paramount to the Company and based on the Company commitment to provide better service quality to its clients, a thorough emergency response system that includes precaution measures to help fighting against COVID-19 and its potential to negatively impact the business was created. Furthermore, The Company COVID-19 Management Standard set the minimum requirements, to manage the risks associate to the COVID-19.
A Crisis Management Team was formed to oversee the implementation of COVID-19 decisions and raise awareness within the Company. All field personnel were required to comply to COVID-19 precautions during operations. With the implementation of new procedures, we were able to maintain the lowest confirmed case numbers among all the companies in our operational sector. Though it was challenging at the beginning for employees to comply, the new requirements have become the normal standard today.
These new measures focus on the importance of employee health and safety when working through COVID-19 pandemic and has helped to educate people on the importance of following health and safety procedures as way of life for everyone while maintaining the Company Operational Sustainability.
Improving ESP Performance in Low Productivity Gassy Wells: Case Study
Authors: A. Salah Ahmed Salah , A. Sabaa Ahmed Sabaa , A. S. Abd Elhaleem Ahmed Samir Abd Elhaleem , Shehab A. Shehab Ali , S. Ritondale Sergio Ritondale , A. Sorou Amin Sorour , A. Nady Ayman Nady 
Published: February 2022

AbstractThe electrical submersible pump (ESP), has become the most efficient and reliable artificial-lift method worldwide. However, in wells with high gas volume, they are highly affected by gas bubbles flowing through each component, affecting: the motor temperature, lifting efficiency, the integrity of the motor and cable. Thus, High gas interference might cause multiple intermittent shutdowns due to Gas Lock and eventually shorten the run life of the ESP.ESP at Well #A was first installed and directly had a problem with High Gas Interference resulting in a pre-mature ESP failure and costly well intervention to restore well-deferred production. A detailed discussion with a technology provider and reference case studies come up with an integrated solution to have the proper gas handling system in addition to adjusting VSD logic to operate on PID current mode. The present study shows a successful ESP optimization through VSD settings in combination with a comprehensive gas handling system that allowed management of low productivity high GOR ESP applications.This gas handling system for ESP was complemented by installing [PMM motor, tandem gas separators with vortex technology, compression mixed-flow stages in a wide range centrifugal pump, advanced gas handler (AGH), and Monel armor leaded power cable]. The system was able to manage GOR&gt;2,000 SCF/STB with WC of &lt;10%, Productivity index of 0.2 BPD/psi, and 75% of free gas flowing into the pump intake. This paper contains all the descriptions of each component in the ESP gas handling system. The system was able to increase well production by reducing gas-locking production shutdowns, stabilizing motor current fluctuations, increasing ESP lifetime, and increasing reservoir life by increasing drawdown and allowing effective pump operation at lower intake pressure.The same directions were successfully applied in another two wells with low productivity gassy behavior; Well #B and Well #C. And, by monitoring ESP performance for a longer period, the pump showed a stable operation, by successfully mitigating the high gas interference trips. Thus, the implementation of this integrated solution in wells with high GOR has been demonstrated to be an effective solution. Also, it provides opportunities to expand the use of ESP in gassy wells, previously thought to be unsuitable.
Automatic Generation of Drilling Schedules Using Machine Learning: A Paradigm Shift in Planning and Resource Allocation
Authors: Nooruddin Hasan , Zahrani Aidah , Shahri Mohammed , Alsousy Abdulhamid 
Published: February 2022

Abstract
In this paper, a new method is presented in which the generation of drilling schedules for planning well cycle requirements are fully automated using advanced machine learning algorithms. The generated schedules incorporate key logics and rules to mimic key operational constrains. The new approach brings about a paradigm shift in planning and resource allocation where time and efforts are reduced by orders of magnitude allowing planners to explore an unlimited number of scenarios and assess plan uncertainty.
Advanced machine learning algorithms are used to automate drilling schedules. A key element is the learning part, in which all rig movements over the past years are carefully tracked and analyzed. Rig capabilities are then inferred and used in prediction, which is achieved by building a Markov Chain (MC) model that tracks the movement of every rig in history and analyses the type of wells drilled in the process. From this, the algorithm computes transition probabilities between different well classes – called MC states – controlling the assignment of rigs to future wells. The MC states can be defined by the user and contain typical well information, such as field and reservoir, fluid type, location, drilling operation type, and well completion type.
The algorithm is tested using a synthetic well requirements dataset, including drilling cost and time estimations over the planning cycle. A detailed drilling schedule was produced from which yearly budget, rig year and well count figures were determined. Since the time to generate a schedule was extremely short, multiple scenarios were performed to address the impact of the total number of rigs to be added every year. This feature permits for resource planning, sensitivity analysis and scenario planning. Quantification of uncertainty in drilling cost and time were addressed by fitting a binormal distribution to historical cost and time values. In each drilling schedule case, drilling cost and time were drawn from the distributions and used to determine overall budget. Tens of cases were performed and histograms of yearly budget, rig year and well count were generated indicating the range of possible figures due to uncertainties in well drilling cost and time.
We present a breakthrough innovation that has a far-reaching impact on planning through automated generation of optimized drilling schedules in minutes. The new approach serves as an important resource for planners by providing the capabilities to explore hundreds of scenarios encompassing entire range of uncertainties.
Extended Physics-Informed Neural Networks for Solving Fluid Flow Problems in Highly Heterogeneous Media
Authors: Alhubail Ali , He Xupeng , AlSinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: February 2022

Abstract
Utilization of neural networks to solve physical problems has been receiving wide attention recently. These neural networks are commonly named physics-informed neural network (PINN), in which the physics are employed through the governing partial differential equations (PDEs). Traditional PINNs suffer from unstable performance when dealing with flow problems in highly heterogeneous domains. This work presents the applicability of the extended PINN (XPINN) method in solving heterogeneous problems. XPINN can create a full solution model to the solution of the governing PDEs by training the neural network on the PDEs and its constraints such as boundary and initial conditions, and known solution points. The heterogeneous problem is solved by performing domain decomposition, which divides the original heterogeneous domain into various homogeneous sub-domains. Each sub-domain incorporates its own PINN structure. The different PINNs are connected through interface conditions, allowing for information to communicate across the interfaces. These conditions include pressure and flux continuities. Various heterogeneous scenarios are implemented in this study to investigate the robustness of the proposed method. We demonstrate the accuracy of the XPINN model by comparing it with the ground truth solved from high-fidelity simulations. Results show a good match in terms of pressure and velocity with errors of less than 1%. Different interface conditions were tested, and it was observed that without the inclusion of pressure and flux continuities, the solver does not converge to the solution of interest. Sensitivity analysis was performed to explore the effects of the neural network architecture, the weights given to each loss term, and the number of training iterations. Results show that wide and shallow networks performed well due to avoiding the gradient vanishing issue that comes with deeper networks. In addition, balanced weights produced better accuracy in general. Moreover, more training iterations improved the accuracy of the results but at lower rates in later training stages. This paper presents XPINN to solve fluid flow in heterogeneous media. We demonstrate the robustness and accuracy of the proposed XPINN model by comparing it with the ground truth solutions in multiple heterogeneous cases. The model shows good potential and can be readily implemented in reservoir characterization workflow.
History Matching and Uncertainty Quantification of Reservoir Performance with Generative Deep Learning and Graph Convolutions
Authors: Shishaev Gleb , Demyanov Vasily , Arnold Daniel , Vygon Roman 
Published: February 2022

Abstract
Generative deep learning is becoming a widely used approach in geological modelling, especially in problems that involve optimization processes under uncertainty like history matching. The basic idea of reservoir modelling and history matching by generative deep learning is to train some kind of reservoir generator and provide optimization under different constraints. There are different approaches that have been developed to utilize various modifications of Generative Adversarial Networks or Variational Autoencoders to implement reservoir generators, but most of them have "conventional" convolution neural networks, hence all data have to be regular (rectangular) in structure. This should be recognized as a limitation.
In this work, we introduce a novel approach of reservoir modelling with Variational Autoencoders based on graph convolutions as opposed to "conventional" convolutions. In our approach reservoir model is considered as a graph, i.e., not a structured data type. Graph convolutions can deal with these data types, and connection with Variational Autoencoders provides us with the capability to generate initially unstructured reservoirs. Variational Autoencoders (VAE) demonstrate the ability to implicitly parameterize geological representations into a latent space of reduced dimensionality and provide ways to uncertainty quantification and production profiling among various geological concepts.
In the first part, we introduce the motivation to Graph Variational Autoencoders (GVAE) as opposed to conventional Generative Deep learning models. The rest of the paper is dedicated to experiments on a synthetic dataset with two different geological scenarios. We show that trained GVAE performs the generation of reservoir models with reliable geology. A latent space structure between Encoder and Decoder of GVAE is represented, and interpretation considering geology under investigation is performed. Finally, we show how latent space can help to estimate the uncertainty of production and an optimization workflow under well data constraints.
Clay Swelling Mitigation During Fracturing Operations Using Novel Magnetic Surfactants
Authors: Tariq Zeeshan , Murtaza Mobeen , Kamal Muhammad Shahzad , Shakil Hussain Syed Muhammad , Mahmoud Mohamed , Al-Jawad Murtada 
Published: February 2022

Abstract
In unconventional tight sandstone formations, due to the presence of clays, the fracturing operations may not be effective because of the clay swelling. The fracturing stimulation is the most used treatment method to produce from such kinds of reservoirs. The swelling of clays caused additional pressure to drop. Prior knowledge about the clay swelling behavior of the rocks with the fracturing fluid can reduce the risk associated with productivity issues. If swelling occurs at the rock's surface, it adversely affects the conductivity of the fractures.
This study proposed novel synthesized magnetic surfactants as a solution for the swelling of clay in hydraulic fracturing treatment. Several coreflooding tests were run using sandstone cores containing a significant proportion of the swelling clays. The permeability was assessed earlier and afterward the coreflooding tests. A scanning electron microscope (SEM) was run before and after the coreflooding experiments. The performances of magnetic surfactants were matched with the well-known clay stabilizing agents including NaCl and KCl.
Permeability measurement after coreflooding experiments revealed 90% permeability lost for the samples mixed with conventional clay stabilizer when post-treated with Deionized Water. On the other hand, the fracturing fluid containing magnetic surfactant solutions as clay swelling stabilizers reserved the initial permeability even post-flooded with DW. It was observed that the conventional clay stabilizers do not provide long-term stabilization of the swelling, while magnetic surfactants retain the permeability for long hours even being interacting with fresh water.
Magnetic surfactants have not been used as swelling inhibitors in fracturing fluids. This is the first study that reports the use of magnetic surfactant in clay swelling inhibition.
3D Inversion and 3D Anisotropy Analysis Improve Real-Time Decision-Making Process in Complex Clastic Reservoirs
Authors: Alkhars Abdulaziz , Achong Christon , Fouda Mohamed , Bikchandaev Eduard 
Published: February 2022

Abstract
Utilizing real-time integration of logging data with innovative 3D inversion and 3D anisotropy datasets, we present an innovative technical workflow highlighting powerful discrimination between oil-bearing sands, water bearing sands, and shale. Successful deployment in several challenging examples robustly reduces the uncertainty of the lithological and fluid analyses in complex clastic reservoirs.
3D Inversion of resistivity and anisotropy improves understanding of fluid distributions and reservoir architecture surrounding the well bore, improving the geological model and distribution of lithology for the field. This can be used to optimize geosteering processes for current and future wells. Integrated interpretation with other LWD data gives qualitative and quantitative information both near the wellbore and within the depth of investigation of the tool. Based on the difference in the Rv/Rh ratio for shale and sand, it is possible to resolve low resistive shale from the Oil Water Contact (OWC) at a distance. Additional interpretation of results provides valuable inputs for completion design optimization and reservoir management strategy updates. This will have significant impact in complex brownfields where reservoirs can possess variable water heights due to production.
Role of Geomechanics in Successful Pilot Evaluation of Unconventional Play in Kuwait
Authors: Gezeeri T. M. , Al-Sabea S. , Shinde A. L. , Perumalla S. V. , Govind Rao D. , Kerrouche N. , Memiche L. , Haddad C. 
Published: February 2022

Abstract
Lower cretaceous unconventional play in Kuwait comprised of majority of Carbonate with a maximum of 15% clay content. Kerogen-rich intervals present in the formation has hydrocarbon potential. However, the rock has very low matrix porosity and permeability and may not flow by itself. This paper discusses the pilot project of this play including formation evaluation and data integration to characterize the geomechanical sweet spots.
Highly deviated well in minimum horizontal stress direction was drilled through this unconventional target formation. No major wellbore instability was noticed while drilling this well. Comprehensive log data acquisition plan was made which included basic logs, image logs, formation pressure measurements, advanced acoustic and deep shear wave imaging. 1D geomechanical model was built by integrating information from drilling, geology, petrophysics and reservoir engineering. This model was calibrated against failure from Image and Caliper logs. Furthermore, wellbore instability events were also calibrated with the model produced failure. Natural fractures were interpreted not only from image logs (on wellbore wall) but also from ‘deep shear wave imaging (away from the wellbore)’. Critically stressed fractures (CSF) analysis was carried out using the fracture interpretations from image logs and geomechanical modeling.
It has been observed that at present day in-situ stresses and pore-pressure profile, some of the optimally oriented natural fractures are critically-stressed (which are hydraulically conductive). Production logging was carried out in an open hole and flow of hydrocarbon was recorded naturally. Hydrocarbon production from production logging matched very well with zones of critically stressed fractures, thereby establishing the role of stress-sensitive natural fractures on production optimization in such low permeable reservoirs.
Using integrated Sub-surface and Geomechanical characterization, hydrocarbon was unlocked from unconventional reservoir in a pilot well. Natural fractures interpretations from deep-shear wave imaging has added significant value and played key role to maximize the production from such low permeability unconventional reservoir. This finding has developed insights on field development strategy that has to include the natural fractures and geomechanics for enhanced production.
Novel Valve Condition Prognostic System for Digitally Enabled High-Pressure Pump Maintenance
Authors: Gerber David , Thaduri Jagan 
Published: February 2022

Abstract
The valve condition prognostics (VCP) system detects anomalies on high-pressure pump fluid-end valves and seats during fracturing before a total functional failure occurs. The VCP enables condition-based fluid-end replacement instead of time-based maintenance intervals, thereby minimizing downtime and maintenance cost and increasing asset utilization while eliminating permanent fluid-end damage due to operating with leaky valves. This is a step change when compared to the fixed-interval maintenance system.
The VCP includes the programmable logic controller (PLC), analog and digital modules, a rotation monitoring encoder connected to the power end, and pressure transducers to monitor fluid-end discharge and suction pressures. The intelligent algorithms feature robust failure prediction criteria based on machine learning [1], pattern classification [2], and adaptive algorithms, applicable to various equipment and field conditions. By obtaining ongoing and accurate pressure signatures, the VCP detects warnings and alarms that are sent to the operators in real time for action. Based on the alarms and operating parameters, the pumps can be shut down automatically to prevent damage.
During multiple field tests, the VCP successfully extended usable valve life by at least 45% when compared to our current fixed-interval maintenance method. The VCP is 100% accurate in detecting a catastrophic valve failure and avoided fluid-end damage. The VCP kits are easy to install onto existing pumps using existing discharge and suction pressure sensors. The data are sent to the cloud, and high-frequency data are recorded in the PLC for detailed analysis as needed.
In contrast to the common replacement approach that is based on either a scheduled time interval or when an operational failure happens, the VCP can detect and notify when an anomaly occurs and performs maintenance only when necessary. The fixed-schedule maintenance approach replaces the valves and seats in a conservative fashion regardless of their condition, often leading to waste. In the failure-based maintenance situation, equipment damage often results, leading to devastating pump shut down and expensive fluid end replacement. The VCP addresses both challenges. It not only prevents prolonged and costly equipment failures, but also reduces downtime, valve and seat parts cost, and maintenance time.