bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation of Pressure-Dependent Permeability Modulus of Tight Sand Reservoir from Multiple Pressure Build-Ups and Its Impact on Production Forecast
Authors: Hammawa Hassan , Aleid Ahmed S. , Alsoqair Heba N. , Alfaraj Jana T. 
Published: February 2022

Abstract
During hydraulic fracturing process of the Permian Basin in North America, the cluster spacing has been shortened to 3m, and stress shadow can no longer be ignored. Many scholars have studied the influence of stress shadows to optimize cluster spacing. For reservoirs with natural fractures, how to activate more natural fractures through hydraulic fracturing has become the purpose. However, few scholars have studied changes in the activation law of natural fractures under stress shadow conditions. This paper establishes stress change value around single fracture according to Sneddon formula, and calculates the maximum and minimum principal stress according to plane principal stress calculation formula. Considering attenuation of net pressure, stress field of multiple fractures is established, and influence of various factors on stress re-orientation is studied. Finally, considering attenuation of net pressure with distance, according to discriminant formulas of tension & shear activation, the proportion of natural fractures that are easily activated is calculated. By designing orthogonal experiments, the influence of different factors on the proportion of activated natural fractures was studied. The stress increase in the direction of the minimum principal stress is much greater than the increase in the direction of the maximum principal stress. The stress increases in the direction of the maximum principal stress at the tip of the hydraulic fracture. The tip position between hydraulic fractures is "neutralized" due to the superposition of shear stress. Stress-fracture angle and the in-situ stress difference are the common main influencing factors for both tensile and shear activation, but the net pressure has little effect on the tensile activation of natural fracture. The fracture spacing has little effect on the activation of natural fractures. When formulating the fracturing scheme, we should pay more attention to the net pressure rather than the fracture spacing.This article provides a fast calculation method for the activation state of natural fractures considering the stress shadow, which provides a reference index for activating more natural fractures and increasing the production of a single well.
Application of Machine Learning in Wet Gas Measurement Predictions
Authors: Sidaoui Ziad , Alsunbul Yasmeen , Abbad Mustapha 
Published: February 2022

Abstract
The Venturi tube, a classic single-phase flow meter, proved to be a reliable and accurate wet gas flow meter, but it requires correction. The presence of liquid content increases the measured differential pressure across the Venturi tube and causes over-reading (OR). Developing novel methods to correct the Venturi tube readings has become a target in the oil & gas industry to quantify production from individual wells. This work is proposing a new approach to overcome the OR challenge using machine learning (ML) models.
The ML model to predict OR was developed using the random forest (RF) technique. Initially, synthetic dataset of producing wells were generated. A variation on the type of gasses and liquids were studied including nitrogen and benzene, argon and water, natural gas and water and natural gas and decan. The input parameters consist of fluid properties and fluid conditions. The fluid properties are including gas and liquid phases. The target for these inputs is to predict the OR. The results were then evaluated based on root-mean-square error (RMSE), and the fitness of the model was assessed by the coefficient of determination R2.
The ML model showed high accuracy results for the OR prediction of the testing data of R2=0.997 and RMSE of 0.7%. The developed model was then applied to a new set of data of air and water for further validation. This validation resulted in R2= 0.998 and RMSE of 0.5%. This shows that the RF technique is capable of predicting the OR in wet gas when the aforementioned input parameters are used. The uniqueness of this ML model is that the inputs are all measurable in the field. Once the OR is predicted, the "real" gas mass flow rate can be calculated directly.
The novelty of this work lies in providing a robust method to calculate the "real" gas mass flow rate real-time which ultimately diminishes the need to use published correlations derived from experimental conditions that are not necessarily representative of the oil field conditions.
Quantifying Wettability Index Using NMR T2 Distributions of Water Phases Extracted with Non-Negative Matrix Factorization
Authors: Li Baoyan , Kesserwan Hasan 
Published: February 2022

Abstract
The wettability of formation is a critically important factor for optimizing the hydrocarbon reservoir production. It is a complicated and challenging issue to quantify the wettability of rock at in-situ conditions. Because of its importance, various methods have been developed to quantify the rock wettability. However, each method has its strength and limitations. This paper presents a new wettability quantification method based on featured T2 distributions of water.
We define a new wettability index to quantify the wettability using NMR T2 distributions of water in wetting and non-wetting phases. We apply the principal component analysis (PCA) method to determine the number of components of the T2 distributions inverted from NMR measurements at different pore pressures. The non-negative matrix factorization is conducted on the matrix of T2 distributions with the determined number of components. A window technique is applied to extract NMR T2 distributions of water in different phases to quantify the rock wettability with the newly defined wettability index.
NMR measurements were conducted on sandstone samples of variant wettability to validate this proposed method. The samples were prepared at different saturation conditions that were achieved using core flooding techniques. The NMR measurements acquired at different saturated conditions of core plug samples were inverted to obtain T2 distributions, whose peak amplitudes and positions indicate sensitivities to saturations and wettability. The wettability indices for the flooded cores were computed in the new wettability quantification method and compared with those from the Amott-Harvey method.
The validation and comparison results show that: Compared with the Amott-Harvey method, the newly defined wettability index can be used to more effectively quantify the altered wettability of a surfactant flooded coreThe PCA identifies that fluid displacement processes (water displacing oil or oil displacing water) result in the shape changes of the T2 distributions inverted from NMR T2 measurements for flooded core plug samples.The non-negative matrix factorization can be applied to extract the feature components of T2 distributions of a flooded core, which are related to the wettability of the core.The T2 distributions of the mobile waters in water-wet pores and oil-wet pores can be extracted from identified components of T2 distributions.
A Novel System for Large Depth-of-Investigation Pulsed Neutron Measurements and Enhanced Reservoir Saturation Evaluation
Authors: Eltaher Yahia , Schmid Gregory 
Published: February 2022

Abstract
Despite its value and importance to oilfield development and reservoir management, carbon/oxygen (CO) logs are commonly associated with significant challenges that are either related to the wellbore logging environment and/or the physics of the measurement. Shallow depth of investigation is considered the greatest challenge related to the nature of the pulsed-neutron (PN) measurement. It can imply a high degree of uncertainty on the measurement and consequently the calculated water saturation, affecting the true assessment of the reservoir fluids’ saturations, especially in challenging logging environments. In this paper we introduce and prove an innovative approach to increase the depth of investigation of the PN measurement.
Currently, all PN logging tools use an electric pulsed neutron generator (PNG), or "particle accelerator" or Minitron, to probe downhole formations with 14 MeV neutrons and record the returning gamma ray signal at a shallow depth of investigation (DOI), which is generally in the range of 7 inches for C/O measurement and 12 inches for sigma measurement. In this new approach, we introduce the idea of increasing DOI of the measured gamma rays through increasing the energy level of the neutrons emitted by a PNG. To prove the concept, a computer modeling and simulation study was conducted using Monte Carlo N-Particle (MCNP) for a pulsed-neutron logging tool to determine DOI for neutron energies higher than 14 MeV.
The study involved five different combinations of borehole and formation fluids. Each involved a "block" of 24 MCNP calculations. The 24 calculations inside each block represented the 24 possible combinations of 3 neutron energies (14, 20, 40 MeV), two gamma ray spectral types (inelastic, capture), and four detectors. Data simulation shows that the DOI rises substantially with energy for all tested detectors. Where the enhancement in DOI with the increase in neutron energy is more prolific in case of the inelastic measurement compared to the capture measurement. And of course the deeper the detector (further from the source) the better the DOI, although this can compromise the precision of the measurement. Yet with the recent technology advancements mainly in PNG (producing more neutron population) and GR detector technology (higher and faster count rates), this shall enhance the precision of the measurement and enable us to acquire both accurate and precise measurements at deeper detectors.
This patented, innovative approach shall significantly reduce and possibly eliminate one of the main reasons behind the uncertainty of reservoir saturation monitoring using PN logs, which is shallow depth of investigation of the measurement. Having a PNG that can produce neutrons at higher energy levels compared to current industry standard shall allow a deeper, more accurate and a representative evaluation of the reservoir.
Inversion Using Adaptive Physics-Based Neural Network: Application to Magnetotelluric Inversion
Authors: Alyousuf Taqi , Yaoguo Li 
Published: February 2022

Abstract
In order to develop a geophysical earth model that is consistent with the measured geophysical data, two types of inversions are commonly used: a physics-based regularized inversion and a statistical-based machine learning inversion. In nonlinear problems, deterministic regularized inversion usually necessitates a good starting model to prevent possible local minima. The neural networks inversion requires large training data sets, which makes its generalizability limited. To overcome the limitation of physics-based regularized inversion and a statistical-based machine learning inversion and combine the benefits of both one inversion scheme, we developed a new physics-based neural network (PBNN) inversion algorithm. In our PBNN inversion, we include machine learning constraints into the regularized inversion using a coupling model objective function. The coupling objective function aims to minimize the difference between the recovered model through regularized inversion and the network-predicted reference model. We update the reference model using either a fully-trained network or an adaptively-trained network. The fully trained PBNN has the ability to collect all of the connections between data and models through a pseudoinverse operator. However, for geophysical inversion applications, particularly in the exploratory setting, this approach is unlikely to become feasible. Neural networks may struggle to extract complicated correlations from data when given insufficient data observations. The technique is impractical for practical usage due to the quantity of training required. In our novel adaptively PBNN algorithm, there is no need to prepare a training data set. At each iteration, the adaptively-PBNN algorithm retrains using the recovered models from the regularized inversion and their related data. The regularized inversion's recovered resistivity models are sufficient to guide neural network predictions towards the true model. One unique advantage is that the approach’s ability to fully use all intermediate models from the regularized inversion that were commonly discarded and apply them to the network training. When applied to synthetic MT data, we show that our technique is capable of reconstructing high-resolution resistivity models.
Permeability Prediction from Clay Volume Using Artificial Intelligence
Authors: Alhamood Hussain A. , Abdulraheem Abdulazeez 
Published: February 2022

Abstract
The importance of permeability can be observed by the massive number of studies on its prediction as reported in literatures. Common methods of permeability prediction depend mainly on core analysis and pressure test data in the lab. However, there are difficulties associated with predicting permeability from traditional methods. This motivates the search for new methods. In this study, the prediction of reservoir permeability from clay volume by using Artificial Intelligence is proposed as an innovative solution.
One of these AI methods is Artificial Neural Networks. The ANN approach is implemented in this paper in order to predict reservoir permeability. This study used the volume of clay and porosity as input parameters. The model data was mainly obtained from well logs and validated by core data from 21CXRM Palaeozoic project in the North Sea. The training of the ANN model was completed using the Backpropagation Neural Network (BPNN). More than 25,000 data points were used in the development of the ANN model. The data was divided randomly to train and test the ANN model.
The results of this study illustrate excellent correlation between the actual permeability values and the predicted values from the ANN model, i.e., 0.9985 and 0.9991 for the training group and the testing group, respectively. The ANN successfully predicted the permeability for a wide range of reservoir permeability values at different reservoir depths. A mathematical equation was developed to demonstrate the relationship between the used input data and the output results. This proves the capability of AI techniques to estimate reservoir parameters in general.
Advances in Virtual Flow Metering Using Deep Composite Lstm-Autoencoder Network for Gas-Condensate Wells
Authors: Omeke James 
Published: February 2022

Abstract
In terms of cost and execution time, data-driven Virtual Flow Meters (VFM) are alternative solutions to traditional well testing (WT) and physical multiphase flow meters (MPFM) for production rate determination which is needed for critical decisions by operators but faced with the challenge of low accuracy due to the transient and dynamic state of multiphase flow systems. Recently, some progress has been recorded by training steady state feed-forward neural networks to learn to approximate production rate based on certain number of input features (e.g., choke opening, pressure and temperature etc.) without any recursive feedback connection between the network outputs and inputs. This disconnection has impacted their accuracy. Dynamic artificial neural network, for example, the recurrent neural networks (RNN), e.g., LSTM has shown good performance as its architecture allows for the usage of data from the past time step to predict the current time step. Forecast accuracy for RNN are limited to short period of time due to their inherent vanishing gradient issues. While majority of VFM application have been developed for oil and gas systems, little or non is applied to gas condensate system.
In this project, a sequence-to-sequence deep composite LSTM-Autoencoders was explored and used to demonstrate the ability of leveraging on its architecture to accurately predict multiphase flow rate for some wells in a gas condensate reservoir with highly dynamic multiphase flow phenomenon. A more complicated flow system was developed using a 3D compositional simulator to simulate, as close as possible, a realistic case of compositional reservoir. A single well was used to train the model and a blind test was ran on two other wells in same reservoir whose data are not part of the training set in order to predict their flow rate with accuracy.
Based on the actual vs predicted results demonstrated, especially the blind test case, the feature extraction and encoding process of the trained LSTM-autoencoder was actually learning the physics of fluid flow and accurately passing the encoded results to the two decoders with very good output (training and testing mean square error are 0.02 and 0.05 respectively).
The ability to leverage on some advanced artificial intelligence framework such as a composite LSTM-autoencoder has proven that it is possible to achieve the desired accuracy needed in data driven VFM to meet the requirement of low cost, low execution time and high accuracy.
This project has also demonstrated the ability of the data driven model to learn the complex dynamics within the temporal ordering of input sequences of production data, with an internal memory adapted to remember or use information across long input sequences, hence, yield longer and reliable forecast, unlike other networks.
Drilling Parameter Based Geomechanical Log Application: A Fracture Stage Selection Case Study in Tight Gas Sand Development Wells
Authors: Bentosa Elena , Makechnie Glenn , Rabines Jaime , Meridji Yacine 
Published: February 2022

Abstract
In heterogeneous tight sand formations, horizontal wells encounter intervals deposited under varying depositional environments along the lateral portion of the wellbore between landing point and total depth. Horizontal wells in this study were drilled in tight sands deposited in a marine environment where lateral depositional facies changes are common, and hydraulic fracture stimulation is necessary to achieve economic hydrocarbon extraction due to the relatively low permeability of the formation. Without geomechanical logs currently derived from wireline logging, it is not possible to optimize cluster spacing and placement. This step provides necesary information used to optimize completion design, which is crucial to the ultimate productivity of a well. Due to formation heterogeneity, expensive wireline logs must be collected in order to optimize fracture stimulation or else new methods to estimate these logs must be employed. This paper presents a technique to optimize cluster selection for hydraulic fracturing in unconventional tight gas development horizontal wells without wireline logging by leveraging Measure While Drilling (MWD) Gamma Ray logs and surface drilling parameters together with Artificial Intelegence (AI) algorythms to predict density, compressional and shear slowness logs for use in geomechanical evaluation.
Oil in Water Monitoring – Data Resolution
Authors: Oulman Spencer 
Published: February 2022

Abstract
Water processing is a widespread challenge for oil production. In many areas of the world, access to water is becoming scarce and cost of water processing is increasing. With aging reservoirs, larger water cuts are expected. The oil production from wells becomes more challenging as larger percentages of water are recovered from production. Added chemistry and extra mechanical work on the fluids become necessary to recover the desired oil. Further downstream of the oil separation, additional work on produced water becomes necessary to further recover oil. Later in the process as the produced water is prepared for disposal or reuse, the trace oil is monitored for recovery to further increase production gains and avoid added costs for disposal or premature fouling of reservoirs in secondary and tertiary enhanced oil recovery operations.
To control the process, oil in water concentration is monitored. The traditional method of obtaining oil in water concentrations is from manual grab samples. These samples are then analyzed predominantly by gravimetric or photometric methods in a lab. An alternate method for obtaining the process results uses online monitoring that is directly connected to the process for high frequency sampling and measurements. The use of oil in water online monitoring has been commercially available for a few decades. Online monitoring has been gaining traction as the cost of the technology becomes economically viable.
The study will focus strictly on the resolution of data to show detail missed by traditional manual grab samples. This study and results will not focus on comparisons of accuracy of measurement, maintenance, and calibration requirements of online monitoring. An onshore producer in the US studied a production process using waterflood injection. Over the 15 days of study, manual grab samples and online sampling were performed. Process changes will be highlighted across the study, showcasing the importance of resolution.
Don't Lose Your Bha - New Approach to Freeing Stuck Pipe in Obm
Authors: Sindi Rami , Rubiea Mohammed , Anioke Chidieber , Alabdulmohsen Zainab , Fathallah Mohamed 
Published: February 2022

Abstract

Objective
Mesophase is a spotting fluid, along with an organic acid which will serve to remove filter cake matter, equalize differential pressure, and free the pipe.

Methodology
Herein is a case history of a successful application using mesophase technology for stuck pipe incident in lateral reservoir section align with wellbore cleanouts. Stuck pipe is a major operational challenge that causes a significant amount of downtime and high costs during the operation.
Filter cake can significantly cause stuck pipe incidents and reduce the efficiency of the production rate of a well. This paper describes an engineered and cost-effective approach to overcome stuck pipe events. This approach is based on removing emulsions, dissolving acid soluble solids, dispersing non-solute and water wetting all surface.
The mesophase restoration system is a customized, and designed to remove mud damage, in-situ emulsions, oil-based mud (OBM) filter cakes in the well bores.

Results
Selection of an appropriate clean-up procedure is critical in ensuring the productivity of open hole wells, particularly horizontal wells. The mesophase system was pumped ahead after pumping the spacer train. The system contains a proprietary surfactant blend, organic acid, corrosion inhibitor, and brine that are all spotted into the stuck spot in the hole and allowed to soak. During this soaking process, the ultra-low interfacial tension properties of the breaker solution begin to diffuse, solubilizing the oil in the emulsions and dissolving the filter cake while dispersing undissolved solids and allowing the pipe to be freed.

Novelty
The success of freeing a stuck-pipe depends on two main factors – the correct diagnosis of stuck-pipe event either to be differential or mechanical stuck, and the right decision to implement the remedial treatment quickly and effectively. Applying the mesophase fluid technique saved the non- productive time (NPT). This technique is a reservoir friendly and has been successfully implemented in stuck-pipe events in oil-based drilling fluids.