bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Approach to Time-Lapse Logging in Producing Horizontal Wells Using Logging While Tripping (LWT) Technology - A Case Study Review in a Middle East Reservoir
Authors: El Wazeer F. , Berdiyev Y. , Bazuhair M. , Al Kaabi R. , Propper M. , Lentz C. 
Published: February 2024

Abstract
Naturally flowing horizontal oil producers typically suffer from increasing Water-Cut within 5 to 7 years of commencing the production. This paper will describe the application of Logging While Tripping (LWT) technology in identifying water-producing sections and hence isolate them effectively by installing Adaptive Inflow Control Devices (AICD) in the permanent completion.
This paper will describe the method of deployment of LWT technology which can measure resistivity, caliper, density, neutron porosity, and gamma-ray in the open hole lateral section from inside the drill pipes. LWT logging tools are pumped down inside the drill pipes and locked into place in the LWT collars and data will then be acquired while tripping out of the well with virtually no extra rig time required. This paper will also describe how this technology can be used in time-lapse logging to be compared with the original saturation profile at well day 1 and identify new water sweep sections in the horizontal oil production wells.
This approach has been applied in more than 10 horizontal wells in the Middle East; timelapse logs have been successfully used to install AICD devices and improve oil productivity. Results from some wells shall be presented. The study showed that with LWT, it became possible to monitor the horizontal/deviated producing well’s water saturation with the following advantages when compared with the conventional method:Using LWT reduced well intervention time by not less than 200% and hence it also significantly reduced the overall well intervention cost and all the associated non-productive time (NPT) and lost-time incidents (LTI).Measuring time-lapse saturation with deep resistivity tools with a depth of investigation of 50-60 inches; which is greater than the conventional sigma saturation tools of 15-17 inches.Measuring time-lapse porosity to observe the effect of acid treatments if any.Caliper data was used to avoid sections of the borehole where the AICDs most likely will not achieve isolation.Virtually zero risk during logging since the radioactive chemical sources are kept safe inside drill collars and can be retrieved in case of stuck pipes.
Ultimately, using LWT achieved its objective of identifying water sweeping sections, resulting in a successful and economic reduction of water.
This paper illustrates a case study for the first time for the successful implementation of LWT in water zonal isolation by time-lapse logging. The paper will open the door for a wider range of LWT applications in high-risk wells delivering significant optimizations and improvements to the well intervention operations.
Characterization and Numerical Simulation of Remaining Oil in Water-Wet Sandstones Based on Micro-CT Displacement Experiment and VOF Model
Authors: Ma Kuiqian , Li Chao , Liu Zongbin , Lu Yan , Huang Jianting 
Published: February 2024

Abstract
Eocene sandstones in the Huanghekou Depression, Bohai Bay Basin, China are characterized by highly variable macroscopic petrophysical properties and heterogeneous microscopic pore structures, and thus control fluid flow behavior and remaining oil distribution in porous rock. In this paper, X-ray CT coreflooding experiments were performed on three sandstone samples with different petrophysical properties. In addition, an integrated approach based on image processing, pore network modeling and the traditional volume of fluid (VOF) model served to quantify the fluid flow behavior and remaining oil distribution in porous rocks. Pore throat size and pore connectivity are the key factors controlling the fluid flow in porous rocks. The water phase advances rapidly along the dominant fluid flow channels and results in a rapid increase of oil displacement efficiency in the sandstones with good pore connectivity. The water flooding rate is also a vital factor affecting the fluid flow by controlling the number of capillaries. The findings provide useful insights into the variation of remaining oil during water flooding process and the fluid flow behaviors in heterogeneous sandstone.
Experimental Research of Imbibition Law in the Continental Shale in Sichuan Basin in China
Authors: Zhou Chao , Chen Yashu , He Zuqing , Wang Zhifa , Zhang Wei , Xu Yuzhu 
Published: February 2024

Abstract
Marine shale has been developed successfully in China, however, the exploration and development of the continental shale is still limited. Study about imbibition and influence factors of the continental shale is insufficient. The objective of the proposed paper is to design and conduct an imbibition experiment to research imbibition rate, imbibed volume, induced crack and influence factors in the Jurassic continental shale in Sichuan basin in China. The imbibition experiment is developed based on the low field Nuclear Magnetic Resonance(NMR) and accurate weighting. The permeability, porosity and mineral composition of shale samples of LU Formation and DD Formation are measured and the differences are analyzed. The change of permeability and porosity before and after the imbibition process is set as the evaluation index and the influence factors of imbibition in the continental shale are analyzed. The influence factors include lithology, imbibition fluid, imbibition pressure and clay content. Besides, the wettability of the continental shale is estimated in the experiment. The experimental results show that the imbibition capacity of the limestone sample is weaker than that of shale samples, and the shell limestone interlayer in the continental shale reservoir may inhibit the imbibition and crack propagation in the shale. Oil phase may enhance the crack propagation after the shale samples induced crack in aqueous phase, and the complicated phase imbibition in the continental shale reservoir may be beneficial to the permeability improvement. The forced imbibition has weaker capacity of crack induction and permeability improvement compared to the spontaneous imbibition, and the influence of the reservoir confining pressure on the imbibition should be considered during the well shut-in process after hydraulic fracturing. The higher clay content shale sample has stronger capacity of crack induction and permeability improvement compared to the lower clay content shale sample. The wettability of the continental shale sample is water-wet. The imbibition experiment reveals the imbibition law and the induced crack character of the continental shale samples, whose results fill the gap in existing studies and have a theoretical guidance for the shut-in and flowback design in the continental shale reservoir.
Innovative Wellsite Porosity Analysis of Tight Cuttings by NMR
Authors: Ali Farhan , Mheibesh Yazan , Valori Andrea , Al-Hamad Mohammed , Abdallah Wael 
Published: February 2024

Abstract
Knowledge of petrophysical parameters of a reservoir plays a significant role in reservoir formation evaluation. Most of the current information comes from downhole logging or special core analysis after acquiring reservoir core samples. Drill rock cuttings, however, are available continuously over the entire depth of the drilled well. Such cuttings can be used to obtain petrophysical data which can add a significant value to formation evaluation while drilling the well. In the current study, we introduce a workflow to accurately obtain porosity from irregular shaped and small size cuttings using nuclear magnetic resonance (NMR).
A set of 9 drill cuttings were washed-out to remove the mud contamination, sieved in a sand-shaker, and then 25-50 g of each drill cuttings samples were collected for the particle size above 1 mm. The cuttings were stacked before they were centrifuged at elevated speeds, and the produced reservoir fluids were monitored. NMR was used to measure the porosity of the cuttings. However, the main uncertainty in such measurement was always the presence of fluids on the outer surface of the cuttings, making it very difficult to separate or distinguish from the pores’ fluids. Therefore, an additional step was implemented as part of this study, to overcome such difficulties.
The stacked rock cuttings were centrifuged, to significantly remove the excess fluids at the cuttings surface, allowing accurate determination of the total porosity. Using the cuttings particle size and the centrifuge arms-length, a threshold capillary pressure value was obtained. At such threshold pressure value, only the excess fluids are removed, without displacing any of the fluids from the pores. It has been observed that only minimal pressure or centrifugal speed is needed to remove the excess water, upon investigation this speed was found to be 730 RPM which was equivalent to 0.016 psi. NMR measurements were performed before and after centrifugation, a clear differentiation in the signal was observed. The measured NMR porosity at the end of centrifugation cycle was found to correlate well with the measured porosity from the reservoir.
Smart Computational Algorithms for the Prediction of Interfacial Tension between Water and Hydrogen – Insights into Underground Hydrogen Storage
Authors: Kalam Shams , Khan Mohammad Rasheed , Arif Muhammad 
Published: February 2024

Abstract
Hydrogen has the potential to play a critical role in the energy transition economy for the next decade, aiding in decarbonization. Hydrogen has a two-pronged utility in the energy mix by acting as a fuel and supporting the distribution of other renewable sources through electrolysis. Nevertheless, a critical hurdle in achieving autonomous hydrogen-based energy transition is the safe, reliable, and economical methods of underground storage mechanisms. Consequently, this requires comprehending interaction processes between hydrogen and subsurface fluids that can affect the storage capacity with a major role of interfacial tension (IFT). Accordingly, this work used smart computational intelligence methods to delineate IFT predictions between H2 and H2O mixture for various pressure/temperature conditions and density variance.
A systematic approach was adopted to implement predictive models for IFT prediction by utilizing an experimental data set. A comprehensive statistical analysis is performed to achieve model generalization capabilities and improve control over the most relevant input parameters. Consequently, IFT is demarcated as a function of two readily available inputs of pressure, temperature, and calculated density difference. Various smart approaches in this work are proposed by developing an IFT predictor using Support Vector Regression, XGBoost, and Decision Tree algorithms. Machine learning model training is enhanced using a k-fold cross-validation technique combined with the exhaustive grid search algorithm. Post-training, the developed models are tested for reliability using blind datasets reserved for this purpose.
A fair comparison between model efficiency is ensured by using an in-depth error analysis schema that includes various metrics like the correlation of determination, average error analysis, graphical error analysis, and scatter plots. This generates a relative ranking system that weighs various factors to classify one model as the most efficient. For the IFT prediction problem, it was found that the XGBoost was aptly able to yield high efficiency and low errors. This stems from how XGBoost functions map the non-linear relationship between pressure, temperature, density difference, and the IFT. It was also observed that enhanced intelligent model training through multiple techniques resulted in optimized hyperparameters/parameters. Lastly, a trend analysis was conducted to confirm the robustness of the developed XGBoost model.
Anomaly Detection of Sensor Measurements During a Turbo-Machine Prototype Testing - An Integrated ML Ops, Continual Learning Architecture
Authors: Palaniappan Somasundaram , Veneri Giacomo , Gori Valentina , Pratelli Tommaso , Ballarini Valeria 
Published: February 2024

Abstract
Building a reliable Machine Learning infrastructure in the Energy domain is a complex task; indeed, it requires a full data integration, continual learning, continual prediction, and the integration of human feedback. Continual learning is a challenging task due to the risk of machine learning to forget former data while learning from new ones (catastrophic forgetting). We present a Machine Learning Operations (MLOPS) architecture able to perform continual learning every day on more than 500 models, to perform inference on new data using such models and to take human feedback and data shift into account. More in detail, we continuously train a recurrent Deep Neural Network to build a virtual sensor from other signals and we compare the prediction versus the real signal to raise (in case) an anomaly. Furthermore, Kullback-Leibler (KL) divergence is used to estimate the overlap between the input distributions available at training time and the distributions seen at test time to estimate the confidence level of the prediction. Finally, we integrate human feedback to tune model retraining. The tool has been applied on a set of about 500 sensors on a three-months long test campaign. Results report 100% recall and 94% accuracy. Moreover, using a recurrent neural network, the system is self-explainable (XAI) by design: indeed, the user can compare the predicted vs real signal to understand the performance of the model. We propose a data intensive MLOPS architecture integrating continual learning, anomaly detection and user feedback. The architecture is based on a standard cloud, it is event-driven and integrates retraining based on GPU capabilities.
Formation Evaluation Through Stuck Heavy Weight Drill Pipe for Drilling Optimization: A Case Study
Authors: Adedamola A. , Ijeh O. R. , Ibironke O. , Afolabi A. , Guo W. , Oaiya L. , Chiroma A. A. 
Published: February 2024

Abstract
Formation evaluation plays an important role in the planning and design of a well. Understanding subsurface reservoir and fluid properties are critical in the efficient management of subsurface resources. During drilling, several challenges including failure of Logging While Drilling sensors could limit the gathering of formation evaluation data, particularly well logs. In this paper, a detailed case study is discussed showcasing an unconventional application of pulsed neutron logging for drilling optimization.
A multidetector pulsed neutron tool was logged through stuck heavy weight drill pipe (HWDP) in an unknown environment with limited data. Significant focus was placed on real time data monitoring to ensure high amount of neutron interaction could be generated for measurement. Pulsed neutron data was acquired in both inelastic and capture modes. Due to the wellbore environment, customized environmental fan charts were modeled to specifically correct for the impact of the heavy mud and HWDP to assess the response from the formation behind the pipe.
The pulsed neutron data was acquired along with gamma ray across the newly drilled formations through the HWDP. The natural gamma ray data was highly attenuated and thus, could not be used for lithology identification. Sigma data along with pulsed neutron porosity indicators were used for lithology identification, shale volume estimation and porosity calculations. Slow capture data from the third detector of the pulsed neutron tool combined with inelastic count rate was used for gas saturation analysis while carbon-oxygen data was used to evaluate the presence of oil. Proper environmental corrections were applied during data analysis using pre-modeled fan charts for the specific wellbore environment. The results of the interpreted pulsed neutron data confirmed the presence of light hydrocarbons within the encountered reservoir along with the associated pay thickness, saturation and formation properties. Consequently, invaluable data was gathered in the untested fault block within the field despite the attendant challenges. Furthermore, drilling operations and recovery plans were optimized based on the pulsed neutron interpretation, thereby avoiding unnecessary well costs associated with making wrong decisions.
This paper demonstrates the application of pulsed neutron logging for formation evaluation through heavy mud and stuck heavy weight drill pipe in order to make efficient corporate management and drilling decisions. The impact of the wellbore drilling environment was assessed real-time to ensure logging parameters were optimized. The value of pulsed neutron logging in this unconventional application was successfully illustrated in this case study despite the limited data and unfavorable logging conditions.
Machine Learning-Assisted Prediction of Electrical Properties for Heterogeneous Reservoirs: Case study in Mamey Field, Northern Colombia
Authors: Moya C. , Prado R. , Caycedo H. , Perez D. , Jimenez C. , Martel L. 
Published: February 2024

Abstract
Petrophysical characterization in reservoirs with high heterogeneity is always a challenge and the application of state-of-the-art technologies plays an increasing role in achieving accurate and timely results.
The target reservoir is composed of interlaminated mudstones and very fine to fine sandstones enclosed in a deltaic environment and capped by cross-stratification sandstones associated with incised valley deposits. The depositional environment creates the geological and petrophysical complexity of the field.
This case study describes an automatic learning technique or "Machine Learning" to determine the electrical properties "m" and "n". The methodology combines logs, rock types, facies and digital core analyses from Mamey field, Lower Magdalena Valley basin, northern Colombia.
An artificial neural network (ANN) methodology based on a self-organizing map (SOM) was applied. The ANN was trained via unsupervised learning to produce a low-dimensional discrete representation of the input space of the training samples called a map. The method does achieve a reduction in dimensionality and creates a continuous curve. An SOM output is typically a branch based on the cluster.
The results obtained indicate that the machine learning technique is feasible to estimate a continuous curve of the Archie parameters "m" and "n" associated with the textural changes identified in the images and computed tomography. The variables "m" and "n" strengthened the resistivity-dependent water saturation model that affects in-situ gas and hence reserves calculations. In addition, the constructed rock electrical properties model reduces costs associated with special core analyses, especially in highly heterogeneous and complex formations.
Fast and Accurate Determination of Reservoir Fluid Interfacial Tension Using FTIR Spectroscopy
Authors: AlRadhwan Azizah , Al-Hamad Mohammed , Abdallah Wael 
Published: February 2024

Abstract
The interfacial tension (IFT) between crude oil and water is an important parameter that helps to understand oil reservoir behavior for reserve estimation to fluid distribution and its recovery mechanism. IFT is usually determined in laboratories, where established methods are used, such as the pendant drop technique. Measuring an accurate IFT, however, can be difficult because IFT is highly sensitive and can suffer from contamination. The IFT experiments for crude oils require a long time for equilibrium and decay stability. In this study, we explored and developed a quick method to derive the IFT of crude oils, with minimal oil sample volume needed, using the Fourier transform infrared (FTIR) spectroscopy technique. Initially, IFT was measured against deionized water for 13 crude oil samples using the pendant drop technique, and then crude oil samples were analyzed with the FTIR spectroscopy tool. From the results, a strong relation was found between the measured IFT values and the FTIR spectroscopy data, allowing us to build a correlation with a strong coefficient of determination (R2= 0.9029) to predict the IFT of dead crude oils at ambient conditions. The correlation was further validated using eight new crude oil samples, where it showed good results with an average error of less than 1.0 mN/m.
Improving the Quality of Water Saturation Calculation Based on Resistivity Logs for Field Under Steam Injection - Jaguar Field
Authors: Mastoadji Erry 
Published: February 2024

Abstract
Jaguar field is one of the largest oil field producers in the Central Sumatra Basin. Currently, there are more than 11,000 wells drilled in the field and it has been developed by steam flood enhanced oil recovery since the 1980s. Steam flood injection and production performance are being monitored through regular cased hole reservoir surveillance acquired from around 900 Temperature Observation Wells (TOWs). Dynamic reservoir properties of temperature, gas/steam saturation and remaining oil saturation monitored through these TOWs.
In reservoir under steam injection, water saturation calculation based on open hole log (resistivity) will be very challenging. Because the presence of steam in reservoir which makes temperature higher, will affect resistivity value in reservoir, either resistivity log from wire line acquisition or water resistivity (Rw). Resistivity of a saline solution measured at two different temperatures reads differently: it will read lower in high temperatures; higher in low temperatures. The accuracy of resistivity value later will affect water saturation result.
Currently we used formation temperature in Jaguar field based on Gradient Geothermal based on internal study (2004). The formation temperature from this equation gives low temperature, even lower than maximum reading temperature from open hole log. This will make Rw value in the reservoir is higher than it should be. With Rw value higher, oil saturation calculation result will be more pessimistic.
To get the more reliable oil saturation calculation result, we need to get information of temperature that is closer to the actual formation temperature in the reservoir. Several alternative temperatures can be obtained and used to replace the formation temperature of the geothermal gradient.