bluuebunny/crossref_metadata_2025 huggingface dataset:
Maximization of the Productivity Index Through Geometrical Optimization of Multi-Lateral Wells in Heterogeneous Reservoir System
Authors: Amin Rana S. , Abdulwahab Ibrahim M. , Rahman N. M. Anisur
Published: February 2024

In the recent decades, it is becoming the new norm to drill multi-laterals wells for better reservoir coverage, improved productivity index, and superior financial return on investment. Optimum design and placement of this type of wells can be studied through rigorous modeling. This study presents a multi-parametric optimization approach that optimizes the design of multi-lateral wells and maximizes the contact with highly productive hydrocarbon zones in the reservoir.

Advanced transient numerical models built in 3D by incorporating the dynamic data into the geological model to mimic the transient-pressure behaviors of multi-lateral wells for a given set of geometrical parameters. Such compliant models with dynamic data indeed capture the reservoir description and dynamics. The optimization process is subject to a number of input variables, such as maximum number of laterals, minimum spacing between wells, and maximum lateral length based on the reservoir characteristics. The multi-parametric optimization generates multiple realizations with different patterns. The productivity index of each pattern is calculated to look for the best multi-lateral well to be drilled.

This study presents a numerical methodology of geometrically optimizing multi-lateral wells. Several local optimizations are performed around each main wellbore to place the lateral sections, and to determine the number of lateral sections. Main objectives of all these realizations are to minimize the competition among the lateral sections, and maximize the drainage area, which do subsequently affect the well productivity index. In permeable reservoirs, interference or competition among the lateral sections comes very quickly, and however, in tight reservoirs, such an occurrence is delayed. After running each realization, well productivity index is calculated, and the productivity index graph is generated against each constraint based on the advanced transient numerical model. Graphical presentation of the productivity index helps decide on the best optimum multi-lateral design. Additional sensitivity analysis is presented to show the impact of reservoir heterogeneity, lateral-section lengths, lateral spacing, number of lateral sections, and the impact of offset producers or injectors.

This workflow will help design the most optimum multi-lateral well with a maximum productivity index under different reservoir conditions in an actual dynamic environment. The proposed workflow has been tested successfully.
Deep Learning-Aided Dull Grading Scheme for Drill Bits
Authors: Sun Jia , Guo Huijuan , Zhang Jiawei , Wang Baodong , Zhang Quanli. , Yang Xiongwen
Published: February 2024

Drill bit design plays a key role in drilling operations. A suitable choice of the drill bit parameters (such as the profile shape, the orientation, and the distribution of cutters, etc.) will significantly improve the durability and stability of the drilling process, increase the rate of penetration (ROP) and enhance the drilling efficiency. Traditional strategies of the drill bit design usually involve a trial-and-error process to adjust the parameters, which is usually time-consuming and contains an element of subjectivity. Alternatively, machine learning techniques provide powerful tools to make full use of the collected data, and analyze the relation between the design parameters and the performance of the drill bits on specified rock mechanics data. In the present work, we aim at building a machine learning-aided scheme to help design the parameters of the drill bits.

A machine learning-aided scheme is proposed to perform the drill bit design. First, a dull grading system is established to evaluate the wear extent of the drill bits from images. The grading system includes a cutter localization model to separate the cutters from drill bit images, a reconstruction model to regenerate the 3D geometries of the cutters from several 2D images, a classifier to evaluate the wear extent of the cutters, and an aggregative algorithm to grade the worn drill bits. The grading system is then applied to build the data set of the worn drill bits. Finally, an individual deep learning model is adopted to learn the relation between the design parameters and the wear extent, and a sensitivity analysis is performed to optimize the design parameters.

The research results showed that: (1) the proposed machine learning-aided scheme could produce instructive guides on the drill bit design, reduce the analysis cost and provide accurate prediction on the impact of the design parameters. (2) The 3D reconstruction algorithm could efficiently extract the geometry feature of the drill bits from 2D data, and provide detailed geometry parameters of each cutter. The performance of the 3D reconstruction model is mainly related to the number of corresponding 2D images of a given drill bit and the coincidence degree among them. (3) The classifier based on convolutional neural networks (CNN) could provide accurate wear extent assessments of drill bits.

In this paper a novel machine learning-aided drill bit design scheme is proposed to effectively enhance the efficiency of the evaluation and optimization of the design parameters. A 3D reconstruction method is established to extract the 3D geometry parameters of the drill bits from several 2D images. The deep learning-based dull grading model is proposed to accurately evaluate the wear extent of the drill bits.
Quality Assessment of Downhole Reservoir Fluid Sampling by Predicted Interfacial Tension
Authors: Al-Hamad Mohammed , Matter Tariq , Ma Shouxiang Mark , Abdallah Wael
Published: February 2024

Acquiring a representative reservoir fluid sample is critical for reservoir engineering, production, and petroleum economics. Because a well may be drilled with different drilling fluids, reservoir oil sampled could be contaminated by drilling fluid used. Although some contamination can be characterized relatively easy, others may be difficult to identify and quantify, which could affect decision making. In this paper, we present a methodology to assess reservoir sampling quality with interfacial tension (IFT) prediction while sampling.

In this study, live reservoir crude oils were sampled from different reservoirs, and their basic PVT properties were measured, which showed distinct properties in density, viscosity, and gas-oil ratio. IFT of the live crude oils was measured against that of the respective formation brines, at reservoir conditions, using a specially designed high-pressure/high-temperature cell, and the measured values range from 18.02 to 31.10 mN/m. Using the measured data of crude oil properties and IFT, an empirical model was developed to predict IFT from basic properties, including oil and water density, oil viscosity, and reservoir pressure and temperature, all can be measured downhole using a formation sampling tool. The model was validated using downhole measurements where IFT was predicted, and results indicate that the IFT prediction is very sensitive to drilling fluid contamination and this phenomenon is then used effectively to identify drilling contamination of the sampled reservoir oil.

The findings of this study provide a new methodology to assess the quality of downhole fluid sampling and therefore reduce operating cost and provide quality reservoir fluid samples. The methodology can be integrated as an answer product of a formation tester to identify contamination and confirm the accuracy of the in-situ measured properties, in addition to minimize pumping-out time, saving rig cost and reduce operational CO2 emissions. In addition, the findings of this study provide a new correlation to drive IFT downhole and at reservoir conditions, through the developed correlation.
Determination of Water Saturation and Porosity in a Carbonate Formation Using Mud Logging Data
Authors: Alkhafaji M.
Published: February 2024

The economic and operational limitations in formation evaluation based on well logging operations have revealed the exigency of a technique that provides detailed information on the target formations. At the same time, it is more economical and less troublesome in different well designs and operational environments. However, there was always room for uncertainty in the quantitative application of mud logging in formation evaluation. Thus, the mud logging industry has introduced more developed and improved systems of mud gas acquisition and data analysis that can provide high-quality information about the subsurface formations. This paper discussed a new approach to determine real-time porosity and water saturation from mud logging data in a cretaceous carbonate formation. Drilling, mud logging, and wireline logging data, from four wells penetrating the Mishrif reservoir, were used. The results showed a good match between the interpretation resulted from mud logging data and that from wireline logs data. Feasible applications of this technique can be very useful in cost reduction and optimizing the design of logging programs for unstable hazardous formations and horizontal development wells where it is risky and costly to run open hole logs. Furthermore, it can be used as a potential tool for evaluating low-resistivity reservoirs.
Time-Dependent Surface Mechanical Properties of Shale During Acid Fracturing: Microindentation Insights
Authors: Rached Rached M. , Haider Syed , Ghreizy Abdullah , Maalouf Elsa
Published: February 2024

Acid fracturing is used to enhance oil and gas production in tight carbonate formations. Fractures are created, and flow paths are etched on the surfaces, allowing for sustained flow under fracture closure stress. The induced fractures aperture and stability depend on the time-dependent deformation of the contacting asperities. In this study, we investigate the effect of acid exposure on the time-dependent deformation of the rock surface using micro-indentation testing.

Microindentation testing is used to investigate the change in the mechanical properties of cores extracted from the Eagle Ford surfaces following acid exposure for 1 hour and 24 hours. Specifically, time-dependent effects are captured by applying a constant load to a small area of the rock surface for a set period of time. Contact creep compliance rates are calculated from the measured deformations and contact areas. Our tests are conducted on both unaltered samples and samples exposed to varying acid strengths for different time intervals. We conduct multiple measurements on each core surface to minimize the effects of grain-scale heterogeneity.

The results demonstrate that the contact creep modulus varies in a fluid- and time-dependent manner. Specifically, a one-hour soak in deionized (DI) water or weak acid (pH=4) led to an increase in the contact creep modulus, signifying reduced creep. Conversely, a sharp decrease was noted after a one-hour soak in a strong acid solution (pH=2). Additionally, it was observed that creep deformation varies with the logarithm of time. Unlike previous studies, the data indicate that the relationship between acid strength and soaking time on creep behavior is complex and merits additional scrutiny, particularly for extended soaking periods in strong acids. These observed trends have significant implications for the long-term behavior of fractures, influencing factors such as proppant embedment and the rate of fracture closure under varying fluid exposure conditions.

This study reveals the critical role of coupled hydro-chemo-mechanical processes in controlling fracture closure during acid fracturing. The significant effect of creep on closure is highlighted, with novel experimental workflows employed using microindentation. Our study provides new insights into the behavior of shale under acid exposure and offers a framework for further research in optimizing acid fracturing.
Impact of Heterogeneity on CO2 Storage in Carbonate Saline Aquifers
Authors: Nolting Andrea F. , Eleftheriou George A. , Alqassab Hisham M. , Terrell Martin J.
Published: February 2024

This paper provides an investigation into the feasibility of carbon storage in saline aquifers, with a specific focus on the potential of injecting CO2 into heterogeneous carbonate rock aquifers that feature both conventional pore type system (referred to as the matrix) and secondary dissolution pore systems (non-matrix). Key areas of interest include the storage potential of the saline aquifer, injectivity of the wells, and the shape and size of CO2 plumes at the end of injection as well as several decades after injection.

To explore the potential of carbonate aquifers for CO2 storage and investigate the complex nature of carbonate rocks on CO2 migration and storage efficiency, the investigation builds twelve distinct geological scenarios that entail varying the quality of matrix systems into three distinct matrix sets: low-quality, moderate-quality, and high-quality matrix porosity and permeability properties. In addition, different non-matrix abundance is superimposed on the three matrix sets, producing significantly different levels of permeability contrast between matrix and non-matrix pore systems. Sensitivity of the results to fluid properties and rock-fluid interactions, such as different scenarios of displacement functions, salinity, and vertical transmissibility, are also tested and benchmarked.

The study demonstrates that non-matrix properties and distribution plays a crucial role in both injectivity and CO2 plume size, particularly in low-quality matrix conditions. The research also highlights that geological heterogeneity significantly affects plume growth and vertical distribution, particularly when there is a substantial contrast between matrix and non-matrix reservoir properties. Additionally, the most effective storage efficiency and minimal plume overprint occur when the matrix is of moderate quality (8%-24% porosity) and non-matrix features are present in low abundance.
CO2 Mass Transfer in Porous Media: Implications for Long-Term Carbon Sequestration in Saline Aquifers
Authors: Basilio E. , Addassi M. , Truscott T. , Hoteit H.
Published: February 2024

Carbon sequestration in saline aquifers is considered a suitable alternative for reducing anthropogenic carbon dioxide (CO2) emissions into the atmosphere. As supercritical CO2 is injected into the subsurface, its density difference with the residing water causes a CO2 buoyant plume to overlay a water-saturated porous layer. The study of diffusive and convective mechanisms during CO2 sequestration in aquifers is paramount for understanding the effects of solubility trapping to determine the long-term fate of the injected CO2. We studied the CO2 dissolution in aqueous solutions in porous media by coupling two experimental techniques: (1) constant pressure method and (2) visualization tests. Each test was performed in a closed visualization cell for at least 36 hours. Capillary tubes filled with glass beads of specific grain sizes are saturated with pH-sensitive solutions. CO2 is continuously supplied into the visualization cell to keep the pressure of the system constant by compensating the gas mass transfer into the aqueous phase. The injected gas is monitored in real- time. Furthermore, as the CO2 mass transfer into the aqueous phase takes place, an acidic solution is created, altering the color of the pH-sensitive solution. The change in color is continuously monitored. Our experimental approach allowed us to study the mass transfer dynamics in two different conditions: (1) diffusive mass transfer with the aid of natural convection; (2) diffusive mass transfer with the isolation of density-driven convection. The diffusive mass transfer was described by measuring the velocity of the interface separating the gas and aqueous phases. Natural convection was characterized by the onset time of convection and flow dynamics. We studied the effects of salinity (NaCl) and the impact of grain size in porous media systems. Results show that as average grain size diameter decreases, natural convection has no enhancing effects on the dissolution process when compared to a diffusive mass transfer process alone. We observed that convection onset time increases as grain size decreases, which in turn, diminishes the mass flux of convection. Salinity has been observed to play an essential role in the convection onset time as it affects parameters such as density, effective molecular diffusivity (affected by pore size), and viscosity. This work introduces a novel experimental approach based on coupling pressure-driven and light transmission-driven techniques to evaluate the mass transfer of CO2 in aqueous solutions in porous media. Results obtained in this study are essential to assess solubility entrapment and its long-term storage integrity.
Novel Physics-Informed Machine Learning Approach to Estimate Safe Mud Window Using Petrophysical Logging Data
Authors: Gowida A. , Elkatatny S.
Published: February 2024

The allowable limits of mud weights for drilling O&G wells, known as the safe mud window (SMW), play a crucial role in preventing wellbore instability issues and loss of circulation. The SMW consists of the minimum mud weight for shear failure (MWBO) and the maximum mud weight for tensile failure (MWBD), which are determined by the principal stresses of the formation, including the maximum (Shmax) and minimum (Shmin) horizontal stresses. Measuring these stresses accurately can be achieved through field tests or approximated using physics-based equations. However, obtaining the necessary in-situ geomechanical parameters for these equations, such as static Poisson's ratio and static elastic modulus, is not always feasible for all wells. Furthermore, the existing machine learning models rely on expensive and destructive tests.

To address these challenges, this study investigated the feasibility of utilizing machine learning (ML) algorithms to predict these parameters in a time- and cost-effective manner. New ML-based models employing artificial neural networks (ANN) were developed to predict the SMW limits (MWBO and MWBD) using petrophysical well-log data as inputs. A comprehensive dataset consisting of field test data and petrophysical logging data was collected and extensively analyzed to train the models.

The predictions generated by the developed ANN-based models exhibited a high degree of accuracy, with a mean absolute average error (MAPE) of less than 0.30% when compared to the actual output values. Thes developed models were validated using an unseen dataset, demonstrating remarkable agreement with the actual stress gradient and SMW limit values. The prediction accuracy exceeded 95%, and the MAPE was as low as 0.59%. The statistical analysis of the results confirmed the robustness of the developed equations in accurately predicting the SMW limits, provided that the logging data are available.

The originality of this research lies in its ability to efficiently and affordably predict the safe mud window (SMW), thereby mitigating drilling problems such as borehole instability. The developed models provide a reliable tool for accurately determining the SMW, surpassing the conventional methods that are more time-consuming and costly.
Interpretable Machine Learning for Prediction of Minimum Miscibility Pressure in CO2-Oil System Considering Nano-Confinement Effect
Authors: Wei Bing , He Yujiao , You Junyu , Wen Shuqin , Tang Jinyu
Published: February 2024

The determination of the minimum miscibility pressure (MMP) in CO2-oil systems is critical for modeling CO2-EOR processes experimentally and numerically. Nevertheless, in nano-confined space, the existing experimental and empirical formula methods present limitations regarding the utilization conditions and prediction accuracy respectively. Thus, in this study, a novel approach combining ML model with Shapley Additive Explanations (SHAP) algorithm is introduced, which aims to provide more precise and physically correct estimates of the MMPs considering the influence of nano-confinement.

A database containing MMPs in CO2 injection process under different conditions is firstly established based on 348 samples collected from experimental results and open publications. The input parameters determining MMPs include reservoir temperature, pore size, and oil composition. In this framework, XGBoost and MLP are used to mimic the input-output relations of the database. Then, SHAP is employed to comprehensively interpret the impact of the inputting factors on the MMPs by calculating the SHAP values.

The present study revealed that both the proposed XGBoost and MLP models exhibited R2 score exceeding 80% and demonstrated good predictive accuracy, as evidenced by small MAE, MSE, and MAPE values. Moreover, a comparative analysis of the SHAP interpretation results of the two models revealed that the explanatory patterns of the MLP model were more consistent with established physical laws, thereby rendering it more suitable for constructing an MMP prediction model based on the dataset employed in this investigation. It is noteworthy that although the SHAP interpretation of the XGBoost model did not entirely conform to actual physical laws, the influence of pore size on MMP followed the same pattern as elucidated by the MLP model. Specifically, within the nano-confined spaces, MMP decreased as the pore size decreased, and the pore size played a crucial role in predicting MMP (ranking first in the XGBoost model and second in the MLP model).

The outcomes demonstrate that the developed interpretable machine learning framework, which incorporates the effects of nano-confinement, can accurately predicts MMP under diverse conditions while maintaining the consistency of physical laws. Consequently, this framework offers valuable insights for the implementation and optimization of CO2-enhanced oil recovery processes.
Advanced Regularization Strategies and Applications in Double Complex Surveys of Qaidam Basin
Authors: Tan Chenqing , Ou Rongsheng , Yan Yufei , Li Xiang , Zhao Changlei , Heng Hailiang , Li Rong , Liu Lianlian , Cui Jie , He Jiao , Wu Chuan , Tong Yanming
Published: February 2024

The processing of land surface-seismic survey data in the Qaidam Basin has for many decades been recognized as facing the dual challenges of both surface and subsurface complexity. To obtain improved seismic imaging of the complex subsurface structures, near-surface modelling constrained by near-surface survey information and a series of multi domain noise suppression algorithms targeting specific noise types have been applied achieving good results. However, in response to the ongoing issues of low signal-to-noise ratio and high uncertainty in imaging caused by complex surface and subsurface structures, we consider it important to evaluate approaches to data regularization and interpolation. In this paper we compare and discuss the effectiveness of several different strategies for seismic data regularization and interpolation based on matching pursuit Fourier interpolation (MPFI) and time domain radon interpolation (TDRI). Furthermore, we conclude that in areas such as the Qaidam basin which exhibit both surface and subsurface complexity, the radial domain 5D MPFI can effectively improve signal-to-noise ratio, enhance the continuity of subsurface reflection events, and provide regular sampling at each azimuth direction, which is beneficial for subsequent structural interpretation and azimuth anisotropy analysis.
A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real-Time
Authors: Al-Rubaii Mohammed Murif
Published: February 2024

When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close from each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models name as ECDeffc.m and MWeffc.m with approach was developed to predict ECD and mud weight (MW) from surface drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation co-efficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 0.001%. The two novel models were compared with the artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value that obtained from pressure while drilling tools (PWD). These models can be utilized during well design and while drilling operation to evaluate and monitor the appropriate mud weight and equivalent circulation density for saving time and money by eliminating the need for expensive downhole equipment and commercial software.
Challenges of Laminated Shaly Rocks Evaluation and Fit-for-Purpose Core Analysis Workflow
Authors: Al-Ofi S. , Ma S. , Al-Hammad R. , Zhang J.
Published: February 2024

Evaluation of thinly laminated shaly sand rocks in the laboratory is challenging due to rock heterogeneity and its complex minerology, for fine scale heterogeneities may result in discrepancies of results obtained from different core analysis methods. In this laboratory study, we address those challenges and present a technique to properly evaluate laminated shaly rock properties for more accurate formation evaluation.

Reservoir cores were cut from a US study Beta well. Lithologies are clean and shaly sandstones. Porosity was measured at dry condition with a gas porosimeter. Effects of drying temperature and time on porosity measurements were systematically investigated. The samples were then saturated with brine and porosity was re-measured using a bench top NMR machine. Results of both laboratory measurements were compared and integrated with log interpretations for better understanding of the formation.

Data obtained in this study show that the measured gas porosity is sensitive to drying conditions for core samples of laminated shaly sands. Comparing measurements of gas porosimetry and NMR indicate that some clay and capillary bound water are difficult to remove even at temperature as high as 300 Deg C, while the NMR porosity showed consistent trend with total porosity derived from logs. While total porosity from cores across the cleaner sand and moderately laminated zones showed good agreement with total porosity from logs, microfractures which are often invisible did affect the quality of core porosity measurements, thus core-log integration.

In summary, although porosity is a fundamental rock property, accuracy and representativeness of porosity measurement may not be trivial, especially for laminated shaly rocks which are geologically more complex, mechanically more sensitive to stress, and thus more difficult to handle and prepare from coring to core analysis. As the most commonly used porosity measurement technique, gas porosimeter may not be the best method for porosity measurement of laminated shaly rocks because of difficulties in sample drying, and also cleaning. In this case, NMR porosity may be a better choice. In addition, because of the stress sensitiveness of laminated core samples, measurements under formation stress will ensure core analysis data more in situ representativeness.
A New Downhole Heating Technology Study and Pilot to Improve Steam Quality
Authors: Yang Lilong , Sun Shouguo , Wu Xiaoming , Yuan Aiwu , Zhang Fuxing , Zhao Chao , Sun Zhenyu
Published: February 2024

Abstrct

This article introduces an unconventional downhole heating technology to improve steam quality. We put a long heater installed in coiled tubing consuming electric energy to the well to heat the steam injected from the wellhead to reservoir to increase the steam quality whose depth up to 1600m or larger, so the reservoir need high quality steam can be developed.

The most important part of this technology is the downhole heater. Big power is the first character and it must be easy to operate, so it is difficult to produce. After many times failures, the heater is ultimately produced successfully by pre-installing special electric wire in coiled tube with insulation around them and then drawing the coiled tube in special machine. We call it "monolithic-draw" technology. With this technology, the produced heater can produce enough energy to improve steam quality downhole and it works safely. Matched pull-push equipment and intelligent power adjust module are also developed.

The heater is produced first time and its length is about 200 meters and the power is about 400kW. The diameter is about 33mm and we test the insulation ability and heating speed in lab. After complicated designed test, we get the relationship between steam quality, power and injection speed, so we can adjust it to adequate power to satisfy field need. Field pilot shows it can heat about 20000m3/h air injected into the well to 480°C and heat low quality steam with 50% dryness to higher quality steam at the depth up to1600m- 2500m. At the depth, now with this technology we can get 50% dryness steam at well bottom by injecting steam from wellhead, so many previous undeveloped super heavy reservoirs can be developed economically. Field pilot was conducted in well group 68, the well group production is increased from 33t/d to 69t/d because of the improvement of steam quality and the operation cost is reduced by 41% because there is no heat loss.

This technology is new developed and it benefits to reduce carbon dioxide discharge because the heater can use green electricity made by wind or sun. It will be widely used in deep buried heavy oil reservoir, and it may be a revolutionary steam producer in comparison with conventional steam producer.
Field Development Plan Defined by Technology: Pressure Transient Analysis and Spectral Decomposition Integrated with Production Made the Difference
Authors: Ardhi Z. , Glukstad M. , Rafliansyah A. , Abdali M. Al , Govinda P. , Mohanty R. , Shabibi H.
Published: February 2024

Field XX in North Oman has been the focus of many penetrations targeting deep formations such as the Haima and Amin reservoirs since 1994. The Upper Gharif interval was penetrated by many wells, but it was never considered a pay zone of interest and was left behind casing. Petroleum Development of Oman (PDO)'s Exploration team considered the Upper Gharif (HSGH-G3) sand as a miss pay and carried out a very successful drilling campaign focused on these sands. Six out of nine wells encountered sands making clear that additional work was needed to understand the sand geometries and the trapping mechanism. Several wells were fracked and tested delivering self-flow production of over 500 m3/day. Not all the wells showed the same response. This work focused on two techniques to help define the extend, channel geometry, connectivity, and reservoir behavior: Wide Angle Spectral Decomposition (WASD) and Pressure Transient Analysis (PTA). Detailed seismic mapping on a Wide Azimuth re-processed volume by PDO's processing team, allowed the use of WASD and the definition of sand geometries, orientation, and dimensions. These were calibrated using the existing well penetrations. PTA analysis of two key wells was performed and the model and results were tested against the geometries observed in the Spectral images. The PTA work clearly showed radial flow and the presence of barriers. It confirmed observations of the dynamic behavior (permeability, skin and storage) and was also used to test different models pertinent to the presence of potential micro-fracture networks or cross flow. The next phase of appraisal and development will continue using the WASD images for well placement and calibration and refinement will ensue in case of surprises.
Application Study on 4 3 4-inch Rotary Percussion Drilling Tool in Hard Formation in KSA
Authors: Tu Yulin , Li Bodong , Zhan Guodong , Aljohar Abdulwahab
Published: February 2024

Drilling in hard and abrasive formations has always been a major change in well development in the oil and gas industry. The extremely high compressive strength and abrasiveness of the formations normally result in low rate of penetration (ROP). This paper introduces the use of a commercial rotary percussion drilling tool, 4 3 4-inch Hydro-efflux hammer, and elaborates on the first-of-its-kind field application in a 5 7/8-inch well bore in the vertical section in Middle East. The field test results showed a 21.54% improvement in ROP compared to offset data, leading to significant cost savings. Functional tests on the rig floor demonstrated the robustness of the technology. To gain insights on the dynamics of the rotary percussion drilling, both surface and down-hole drilling dynamics sensors are deployed to measure the vibration for the field test. The vibration characteristics of the hydro-efflux hammer is also investigated, provided valuable data for optimizing the rotary percussion technique.
Enhancing Confidence in Multiphase Flow Profiling with Innovative Downhole Sensors
Authors: Maity S. , Manzar A. , Alshabibi H. , Aljindan J. , Zeghlache M. L. , Bukhamsin A. , Benlakhdar N.
Published: February 2024

Downhole flow profiling is critical to understand performance of a well, maximizing oil recovery and optimize the development of entire field. To address the challenges in multiphase flow profiling and the limitations of conventional fluid holdup measurements in complex conditions, a new Production Logging (PL) solution integrates distributed density, refractive-index (RI), and fluorescence (FL) measurements for more accurate flow profiling and zonal allocation. Innovative optical and piezo helm resonator sensors are developed to overcome traditional limitations, providing independent detection of oil and gas and enabling distributed density measurement, respectively. Promising results from flow loop testing and field deployment are discussed and help to enhance confidence and redundancy in holdup measurements for improved well performance optimization.
Successful Implementations of Drilling Microchips for Distributed Measurement while Drilling
Authors: Dokhani Vahid , Alshaikh Abrar , Li Bodong , Zhan Guodong
Published: February 2024

Access to downhole properties is essential to ensure the safety and efficiency of drilling operations. This study describes successful implementations of drilling microchips to acquire circulating pressure and temperature while drilling. It also elaborates on our developed scheme for depth conversion of the measured data.

In this study, 100 drilling microchips were dropped in three different sections of a wellbore. The microchips were deployed in a single batch in each trial. Although no recovery was achieved in the first trial, the learning lessons were utilized to revise the procedure. In particular, the flow rate was increased to ensure the recovery of microchips. Overall, 35 pieces of microchips were recovered, which shows a success rate of 35%. A data processing method is employed to convert the raw measured data in the time domain into depth scale. The method minimizes the difference between the predicted transit time and the actual transit time, which also takes into account the slip velocity of tracers.

The measured circulating pressure data enables obtaining separate velocity profile parameters for pipe and annuli. The results of measured pressure and temperature data show consistent measurements in pipe and annuli regardless of microchip type. The results further show that the measured pressure data can be used for the calculation of the ECD profile. It is also recognized that the accuracy of ECD calculations is better in lower sections of the wellbore. It is realized that a higher recovery rate is expected with increasing the flow rate. Novel results of successful data acquisition of downhole temperature and pressure are presented. In critical zones where the application of logging tools becomes risky, the presented method for acquiring downhole data is advantageous.
Fluorinated Polyzwitterion Coatings for Stabilizing Colloids and Lowing Retention of Nanoparticles
Authors: Zhu S. S. , Chen H.
Published: February 2024

Nanoparticles (NP) injected in hydrocarbon reservoirs, as inter-well tracers or an enhanced oil recovery (EOR) method, has been of great interest in petroleum engineering. However, NP's retention in reservoir conditions of high salinity and high temperature is one of the challenges in these applications because high retention is economically unfavorable. We explored the concept of applying lightly fluorinated polyzwitterions as a coating on hydrophobic styrenic nanoparticles that had previously shown significant adsorption on rock surfaces. This is a less explored area of research that correlates the properties of polymer and NPs retention to the structure variation of polymers and polymer coatings. The fact that a small fraction of a functional comonomer on a common polymer backbone can significantly change the polymer and NPs retention provides a low-cost option of modifying the retention property of common oil field materials.
Reservoir Engineering Aspects of Geologic Hydrogen Storage
Authors: Bauer J. F. , Amro M. M. , Nassan T. , Alkan H.
Published: February 2024

Safe and effective large-scale storage of hydrogen (H2) is one of the biggest challenges of the global energy transition. The only way to realize this is storage in geological formations. The aim of this study is to address and discuss the reservoir engineering (RE) aspects of geological H2 storage (GHS). The study is based on two sources: first, a comprehensive literature review, and second, experimental and numerical work performed by our institute. The current state of the art regarding the principles of reservoir engineering on the application of GHS is reviewed and summarized.

Atypical properties of H2, with its lower density, viscosity and compressibility factor higher than one, increase uncertainties in the definition of capacity, injectivity, and confinement. In addition, the abiotic and biotic reactivity of H2 should be considered in the associated changes in petrophysical properties and molecular mass transfer in subsurface storage formations. Therefore, both geochemistry and reservoir microbiology are inseparable components of reservoir engineering of GHS. The sealing of H2 storage in a porous reservoir with caprock is due to the interplay between potentially higher capillary threshold pressure but higher diffusivity of H2, while the technically impermeable assumption of most deep salt formations can be considered as valid for H2 storage in caverns. Such changes can also affect the injectivity of H2 through plugging or dissolution. Well integrity is of particular concern when abandoned-old gas wells are reused. Especially at higher temperatures, hydrogen can behave more actively to support metal oxidation processes at the casing-cement contact and microbiological activity can promote these reactions. In addition, the permeability of the hardened cement samples to H2 is highly dependent on the effective pressure.

An overview of the reservoir engineering aspects of GHS is compiled from recent publications. We integrate key findings with our experimental results to provide essential guidance for front-end engineering and challenges to be addressed in future work. Monitoring of the reservoir pressure, as an indicator of microbial activity, is of great importance. Therefore, measures to control microbial activity have to be drawn, taking into account the site-specific characteristics.
Data-Driven Approaches for Quick Performance Assessment of Hydrocarbon Reservoirs
Authors: Alrassan A. I. , Al-Turki A. A. , Al-Zahrani T. M.
Published: February 2024

Hydrocarbon reservoir's heterogeneity, production, injection and aquifer support play a unique role into how field strategies are developed knowing key reservoir properties (e.g. drainage volumes, reservoir energy, rock properties, decline analysis, etc.). Alanood et al. (2022) illustrated applications of Fast Marching Method (FMM) in assessing reservoir performance, identifying reservoir patterns and anomalies from production/injection data, and predicting the reservoir response when considering modeling uncertainty for model calibration. The objective of this paper is to extend the previous work to include more field development controls like voidage replacement, infinite acting reservoirs, and performance prediction of new proposed producers or injectors.

The proposed approach in this work is to extend the physics-aware data-driven approach, that uses the diffusive time-of-flight (DTOF) to determine the pressure disturbance caused by production/injection wells to help in comprehending the impact on field development. The previous work by Alanood et al. (2022) is extended to account for voidage replacement, pressure maintenance and infinite acting reservoirs. In addition, this approach has been extended to predict the performance (e.g., rates, bottom-hole pressure and plateau) of proposed injection and production wells to give a quick insight about their performance prior to running the full-physics reservoir simulators. Thus, will give a better understanding in describing the transient flow. That is in addition to helping in inferring faults existence, fracture networks and inter-well connectivity.

In this work, SPE10 and NORNE benchmark models were used to test and validate the approach. The results show that this date-driven approach was able to match historical and predicted performance of the reservoirs and wells. The data-driven algorithms were able to generate pressure maps resembling the ones obtained from the full-physics reservoir for infinite acting reservoir models for the first few years before it starts to deviate. They also were able to predict rates and flowing bottom-hole pressure of injectors and producers to up to 80% when compared to reservoir simulation. In addition, several blind tests of placing injectors and producers revealed that the algorithms are capable of capturing the reservoir's pressure changes and wells performance in finite and infinite acting reservoirs.

The extended work of the physics-aware data-driven approach using the DTOF is a reliable approach to monitor reservoir pressure in the finite and infinite acting reservoirs (e.g., aquifer support and pressure maintenance injection). It also demonstrated the ability to predict the performance of existing and newly proposed wells to great accuracy. The approach provides a quick assessment of wells and reservoir performance given a certain field development strategy.
Successful Treatment in Water Control with Relative Permeability Modifiers for HP/HT Carbonates
Authors: Petriz José María , García Luis Alejandro , Acuña Erick , Narváez Iván Ernesto , Santos Sarai , Eoff Larry , Estrada Julio Vasquez , López Iván
Published: February 2024

One of the main problems in the decline of the production of any field is associated with the presence of fractional flow of water and its detrimental effect on the wells. This situation has led to a whole field of study regarding the understanding of this phenomenon, aimed at mitigating this through the application of several technologies and best reservoir management practices.

This paper will present the analysis, development and results obtained from the application of a relative permeability modifier (RPM) in a naturally fractured carbonate reservoir under high pressure and temperature conditions (HP / HT). This is in addition to a series of so-called "high potential" interventions, whose production benefits result in a profitable business case applying an optimized shut-in time strategy in high production wells.

Finally, a summary with various results of the application of the RPM in different wells will allow broadening the criteria of its application. Although there are cases of success, there will be others in which a better diagnosis must be made to ascertain its effectiveness in the short or medium term, which may be related to the identification of the preferential means of flow in a naturally fractured reservoir, which by its nature, results in a technical-technological challenge, both in its identification and discretization as well as in the field applications.
Experimental Study on the Use of Nanoparticles to Improve the Efficiency of CO2 Geological Storage
Authors: Ishibashi F. , Kumasaka J. , Esaki T. , Sugai Y.
Published: February 2024

Supercritical CO2 injected into the aquifer in CCS may migrate upward in the aquifer because its density is lower than the density of formation water. As a result, CO2 cannot be stored widely throughout the aquifer, which may reduce CO2 storage efficiency. The objective of this study is to increase the apparent density of supercritical CO2 by dispersing nanoparticles in itself and to improve CO2 storage efficiency by injecting CO2 widely in the aquifers.

The n-hexane, which has a similar solubility parameter to supercritical CO2, was used as an alternative solvent to supercritical CO2 in this study. The nanoparticles such as SiO2, Al2O3, TiO2, and ZnO were added and stirred in n-hexane respectively, and their dispersibility was observed after stirring with a magnetic stirrer or ultrasonication for 30 minutes. The dispersibility of nanoparticles in n-hexane was qualitatively evaluated by irradiating the sample with green laser light after stirring and observing the scattered light. The effect of surfactants on improving the dispersibility of nanoparticles was also evaluated using nanoparticles modified with those surfactants.

All the nanoparticles were precipitated after 30 minutes of stopping stirring with a magnetic stirrer. The dispersibility of those nanoparticles was improved by ultrasonication and the dispersion was maintained up to about 3 hours. Similar experiments were conducted using nanoparticles modified with surfactants. As a result, improved dispersibility of Al2O3 and ZnO modified with anionic surfactants was obviously observed and laser light scattering was clearly observed for about 2 hours after the stirring was stopped. Finally, it was demonstrated that the dispersion of Al2O3 nanoparticles modified with anionic surfactant was maintained even after 48 hours in n-hexane by ultrasonication.

These results indicate that Al2O3 nanoparticles modified with anionic surfactant have high dispersibility in n-hexane, especially with ultrasonication. Anionic surfactants make the surface of nanoparticles hydrophobic, which enhances their dispersibility in nonpolar solvents such as supercritical CO2. Ultrasonication is effective for eliminating agglomeration among nanoparticles and improves their dispersibility in nonpolar solvents.