bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Resolution Micro-Continuum Approach to Model Matrix-Fracture Interaction and Fluid Leakage
Authors: He Xupeng , Alsinan Marwa , Kwak Hyung , Hoteit Hussein
Published: December 2021

Understanding the fundamental mechanism of fracture-matrix fluid exchange is crucial for the modeling of fractured reservoirs. Traditionally, high-resolution simulations for flow in fractures often neglect the matrix-fracture leakage influence on the fracture hydraulic properties, i.e., assuming impermeable fracture walls. This work introduces a micro-continuum approach to capture the matrix-fracture leakage interaction and its effect on the rock fractures’ hydraulic properties.

Because of the multiscale nature of fractured media, full physics Navier-Stokes (NS) representation everywhere in the whole domain is not feasible. We thus employ NS equations to describe the flow in the fracture, and Darcy’s law to model the flow in the surrounding porous rocks. Such hybrid modeling is achieved using the extended Darcy-Brinkman-Stokes (DBS) equation. With this approach, a unified conservation equation for flow in both media is applied by choosing appropriate parameters (e.g., porosity and permeability) for the corresponding domains. We apply an accurate Mixed Finite Element approach to solve the extended DBS equation. Various sensitivity analyses are conducted to explore the leakage effects on the fracture hydraulic properties by varying surrounding matrix permeability, fracture roughness, and Reynolds number (Re).

Streamline profiles show the presence of back-flow phenomena, where in-flow and out-flow are possible between the matrix and the fractures. Further, zones of stagnant (eddy) flow are observed around locations with large asperities of sharp corners under high Re conditions. Numerical results show the significant effects of roughness and inertia on flow predictions in fractures for both impermeable and leaky wall cases. Besides, the side-leakage effect can create non-uniform flow behavior within the fracture that may differ significantly from the case with impermeable wall conditions. And this matrix-fracture leakage influence on hydraulic properties of rock fractures matters especially for cases with high matrix permeability, high fracture roughness, and low Re values. In summary, we present a high-resolution micro-continuum approach to explore the flow exchange behavior between the fracture and rock matrix, and further investigate the static and dynamic effects, including variable Reynold numbers, mimicking flow near and away from the wellbore. The approach and results provide significant insights into the flow of fluids through fractures within permeable rocks and can be readily applied in field-scale reservoir simulations.
Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques
Authors: Al Gharbi S. H. , Al-Majed A. A. , Abdulraheem A. , Patil S. , Elkatatny S. M.
Published: December 2021

Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations.

To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality.

To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.
Uncertainty in Geomechanical Modeling
Authors: Tatur Olga , Ocheretyanyy Anton , Petrakov Yuriy , Sobolev Alexey
Published: December 2021

The active application of geomechanical modeling in the oil and gas industry began in the early 2000s. Geomechanics is used in all life cycles of the field-from exploration drilling to field development models, construction of production wells and monitoring of the development process. The success of geomechanical modeling directly depends on the quantity and quality of input information. In contrast to the geological and hydrodynamic models in geomechanics, there is still no common approach and algorithm for the quantitative determination of the error of the model. This paper presents an algorithm for determining the 1D error of a geomechanical model, taking into account the measurement errors of the devices and the correlation dependencies used. This is the first step towards the formation of a unified approach to the quantitative determination of the error of 1D and 3D geomechanical models and the construction of uncertainty corridors.

In this paper, we propose a mechanism for estimating the error of correlation dependencies, which includes not only the error of the measuring equipment, but also the resulting convergence of the dependence and the rate of growth or decline of this convergence. The confidence parameter is used to estimate the contribution of a particular dimension to the calculations. The sensitivity of the resulting error to the quality of the source information is analyzed. In the application of this technique, it is possible at the initial stage to make a conclusion about the quality of the simulation results and to take measures to improve the reliability of the calculations.
Deep Similarity Learning for Well Test Model Identification
Authors: Nagaraj Guru , Pillai Prashanth , Kulkarni Mandar
Published: December 2021

Over the years, well test analysis or pressure transient analysis (PTA) methods have progressed from straight lines via type curve analysis to pressure derivatives and deconvolution methods. Today, analysis of the log-log (pressure and its derivative) response is the most used method for PTA. Although these methods are widely available through commercial software, they are not fully automated, and human interaction is needed for their application. Furthermore, PTA is described as an inverse problem, whose solution in general is non-unique, and several models (well, reservoir and boundary) can be found applicable to similar pressure-derivative response. This tends to always bring about confusion in choosing the correct model using the conventional approach. This results in multiple iterations that are time consuming and requires constant human interaction.

Our approach automates the process of PTA using a Siamese neural network (SNN) architecture comprised of Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) layers. The SNN model is trained on simulated experimental data created using a design of experiments (DOE) approach involving most common 14 interpretation scenarios across well, reservoir, and boundary model types. Across each model type, parameters such as permeability, horizontal well length, skin factor, and distance to the boundary were sampled to compute 560 different pressure derivative responses. SNN is trained using a self-supervised training strategy where the positive and negative pairs are generated from the training data. We use transformations such as compression and expansion to generate positive pairs and negative pairs for the well test model responses.

For a given well test model response, similarity scores are computed against the candidates in each model class, and the best match from each class is identified. These matches are then ranked according to the similarity scores to identify optimal candidates.

Experimental analysis indicated that the true model class frequently appeared among the top ranked classes. The model achieves an accuracy of 93% for the top one model recommendations when tested on 70 samples from the 14 interpretation scenarios. Prior information on the top ranked probable well test models, significantly reduces the manual effort involved in the analysis.

This machine learning (ML) approach can be integrated with any PTA software or function as a standalone application in the interpreter's system. Current work using SNN with LSTM layers can be used to speed up the process of detecting the pressure derivative response explained by a certain combination of well, reservoir and boundary models and produce models with less user interaction.

This methodology will facilitate the interpretation engineer in making the model recognition faster for detailed integration with additional information from sources such as geophysics, geology, petrophysics, drilling, and production logging.
Relative Permeability Modifiers as a Chemical Means to Control Water Production in Oil and Gas Reservoirs
Authors: Al-Taq Ali , Alrustum Abdullah , Alfakher Basil , Al-Ibrahim Hussain
Published: December 2021

It is challenging to control water production in horizontal wells or in vertical wells having oil and water produced from the same zone using conventional methods such as through-tubing bridge plugs or other mechanical means. Relative permeability modifiers (RPMs), known to selectively reduce the relative permeability to water with minimum impact on the relative permeability to oil or gas, are considered a promising technology for solving this problem. The current generation of RPMs, unlike the old ones, can tolerate high hardness and so have higher success rates.

An extensive experimental work was carried out to evaluate three RPMs for water control in gas and oil wells. Test conditions included gas flow in sandstone cores with temperatures of up to 300°F, and oil flow in carbonate cores with temperatures as high as 220°F. The effect of initial core permeability to brine, RPM concentration, flow rate, and water-wetting surfactants on the effectiveness of RPM to reduce water production was investigated using sandstone and carbonate cores.

Coreflood experiments were undertaken at downhole conditions. The end-point relative permeabilities to various phases were measured. A back pressure of 500 psi, an overburden pressure of 3,500 to 5,000 psi and flow rates of 0.1 to 5 cm3/min were used. The concentration of RPM polymers was monitored in the core effluent using total organic carbon (TOC) analyzer to determine polymer retention in the core.

The results revealed that temperature adversely affected the effectiveness of all RPMs evaluated. A better reduction in permeability to water was obtained at 220°F compared to that obtained at 300°F. The use of RPM at the right concentrations was found to significantly reduce permeability to water. A better water reduction was obtained at higher polymer injection rates, which was attributed to flow-induced polymer retention. Adsorption of RPM polymer tended to alter wettability of a carbonate rock to more water-wet. This paper discusses the effects of the above parameters on the performance of RPM in sandstone and carbonate reservoirs, and it gives some recommendations for improving the success rate of these chemical applications in the field.
Perfecting Straddle Packer Microfrac Stress Contrast Measurements for Hydraulic Fracturing Design in UAE Tight Oil Reservoir
Authors: Franquet Javier , Martin A. N. , Telaj Viraj , Khairy Hamad , Soliman Ahmed , Zabirov Roman , Syofvas Syofyan , Nestyagin Andrey , Fauzi Tengku , Talib Noor , Al-Shabibi Tariq , Banihammad Basma
Published: December 2021

The objective of this work was to quantify the in-situ stress contrast between the reservoir and the surrounding dense carbonate layers above and below for accurate hydraulic fracturing propagation modelling and precise fracture containment prediction. The goal was to design an optimum reservoir stimulation treatment in a Lower Cretaceous tight oil reservoir without fracturing the lower dense zone and communicating the high-permeability reservoir below. This case study came from Abu Dhabi onshore where a vertical pilot hole was drilled to perform in-situ stress testing to design a horizontal multi-stage hydraulic fractured well in a 35-ft thick reservoir.

The in-situ stress tests were obtained using a wireline straddle packer microfrac tool able to measure formation breakdown and fracture closure pressures in multiple zones across the dense and reservoir layers. Standard dual-packer micro-injection tests were conducted to measure stresses in reservoir layers while single-packer sleeve-frac tests were done to breakdown high-stress dense layers. The pressure versus time was monitored in real-time to make prompt geoscience decisions during the acquisition of the data. The formation breakdown and fracture closure pressures were utilized to calibrated minimum and maximum lateral tectonic strains for accurate in-situ stress profile. Then, the calibrated stress profile was used to simulate fracture propagation and containment for the subsequent reservoir stimulation design.

A total 17 microfrac stress tests were completed in 13 testing points across the vertical pilot, 12 with dual-packer injection and 5 with single-packer sleeve fracturing inflation. The fracture closure results showed stronger stress contrast towards the lower dense zone (900 psi) in comparison with the upper dense zone (600 psi). These measurements enabled the oilfield operating company to place the lateral well in a lower section of the tight reservoir without the risk of fracturing out-of-zone.

The novelty of this in-situ stress testing consisted of single packer inflations (sleeve frac) in an 8½-in hole in order to achieve higher differential pressures (7,000 psi) to breakdown the dense zones. The single packer breakdown permitted fracture propagation and reliable closure measurements with dual-packer injection at a lower differential reopening pressure (4,500 psi). Microfracturing the tight formation prior to fluid sampling produced clean oil samples with 80% reduction of pump out time in comparison to conventional straddle packer sampling operations. This was a breakthrough operational outcome in sampling this reservoir.
Healing Seismic Data with Phase Corrections for Processing of Single-Sensor Data in the Desert Environment
Authors: Bakulin Andrey , Silvestrov Ilya , Neklyudov Dmitry
Published: December 2021

Acquiring data with single sensors or small arrays in a desert environment may lead to challenging data quality for subsequent processing. We present a new approach to effectively "heal" such data and allow efficient processing and imaging without requiring any additional acquisition. A novel method combines the power of seismic beamforming and time-frequency masking originating from speech processing. First, we create an enhanced version of the data with beamforming or local stacking. Beamforming effectively suppresses scattered noise and finds weak reflection signals, albeit sacrificing some higher frequencies. Next, we employ a seismic time-frequency masking procedure to fix the original data while using beamformed data as a guide. Time-frequency masking effectively fixes corrupt and broken phase of the original data. After such data-driven healing, prestack data can be effectively processed and imaged, while maintaining the higher frequencies lost during beamforming.
System Analysis and Application of Combined System of ESP and Gas Lift
Authors: Zhao Ruidong , Sun Yizhen , Zhao Hanjun , Shi Junfeng , Zhang Xishun , Wang Peng , Ren Lihua , Wang Cai , Deng Feng , Chen Shiwen , Chen Guanhong , Lyu Yanping
Published: December 2021

With the development of deep-buried reservoirs and offshore fields, many prominent problems have been encountered by the use of conventional single artificial lift technologies, which can not meet the requirements of production and may cause frequent workovers. The combination of electrical submersible pump (ESP) and gas lift system (GL), taking advantages of flexible pump rate, relative long workover intervals and simple composition of tubing strings, is considered to be a better solution.

The design of ESP-GL combined system is more complicated, referring to the distribution of pressure, temperature and viscosity fields of multiphase flow in the tubing string. In this article, based on the performance curves of lift devices and oil well, the design approach of the ESP-GL combined system based on nodal analysis is established with an example calculation. An optimization design approach of the combined system is then developed by intelligent algorithms, considering some key operating parameters, e.g. pump drainage rate, ESP depth, ESP stages, valve depth and gas injection rate, to find the optimal operating condition of the system. At the same time, the combined lifting system has been successfully applied in some pilot tests in China and Vietnam reporting to have production increments, which suggests a good potential for the application of the ESP-GL combined system in deep fields.
Insights on Fractured Domains in Reservoirs Resulting from Modeling Complex Geology/Structures - Case Study of the Ratana Field in the Potwar Basin, Pakistan
Authors: Wrobel-Daveau Jean-Christophe , Barracloughy Rodney , Laird Sarah , Matthies Nick , Saeed Bilal , Shoaib Khalid , Zafar Zaheer
Published: December 2021

Exploration success in fold-and-thrust belts, like the Potwar petroleum province, is impacted by seismic imaging challenges and structural complexity. Success partly relies on the ability to validate subsurface models and model a range of properties, such as reservoir permeability. This is particularly important in the case of tight carbonate reservoirs such as the Eocene Sakesar Formation, where the recovery of economic quantities of hydrocarbons is conditioned by the presence of fracture-enhanced permeability. This requires the application of geological and geophysical modeling techniques to address these challenges, to minimize uncertainty and drive exploration success.

The interpretation and structural validation of the Ratana structure presented here allows the proposal of a consistent and robust structural model even in areas of higher uncertainty in the data, such as along faults. The dynamically updatable, watertight, complex 3D structural framework created for the top Sakesar reservoir was used in combination with an assisted fault interpretation algorithm to characterize the fault and fracture pattern.

The results showed a higher density of high amplitude fractures on the flanks of the structure rather than along the hinge. These results are supported by the incremental strain modeling based on the kinematic evolution of the structure. Together, this helped to characterize potential fracture corridors in areas of the seismic volume that previously proved challenging for human driven interpretation.

Our results allow us to reduce the uncertainty related to the geometrical characteristics of the reservoir and provide insights into potential exploration well targets to maximize chances of success, suggesting that permeability and hydrocarbon flow may be higher at the margins of the Ratana structure, and not at the crest, which was the focus of previous exploration and production efforts.
Dynamic Formation Evaluation to Reduce Uncertainty and Confirm Completed Intervals in Brown Fields
Authors: Kamaruddin Muhamad Aizat , Ashqar Ayham , Suhaimi Muhammad Haniff , Salleh Fairus Azwardy
Published: December 2021

Uncertainties in fluid typing and contacts within Sarawak Offshore brown field required a real time decision. To enhance reservoir fluid characterisation and confirm reservoir connectivity prior to well final total depth (TD). Fluid typing while drilling was selected to assure the completion strategy and ascertain the fluvial reservoir petrophysical interpretation. Benefiting from low invasion, Logging While Drilling (LWD) sampling fitted with state of ART advanced spectroscopy sensors were deployed. Pressures and samples were collected.

The well was drilled using synthetic base mud. Conventional logging while drilling tool string in addition to sampling tool that is equipped with advanced sensor technology were deployed. While drilling real time formation evaluation allowed selecting the zones of interest, while fluid typing was confirmed using continually monitored fluids pump out via multiple advanced sensors, contamination, and reservoir fluid properties were assessed while pumping. Pressure and sampling were performed in drilling mode to minimise reservoir damage, and optimise rig time, additionally sampling while drilling was performed under circulation conditions. Pressures were collected first followed by sampling.

High success in collecting pressure points with a reliable fluid gradient that indicated a virgin reservoir allowed the selection of best completion strategy without jeopardising reserves, and reduced rig time. Total of seven samples from 3 different reservoirs, four oil, and three formation water. High quality samples were collected. The dynamic formation evaluation supported by while drilling sampling confirmed the reservoir fluid type and successfully discovered 39ft of oil net pay. Reservoir was completed as an oil producer. The Optical spectroscopy measurements allowed in situ fluid typing for the quick decision making. The use of advanced optical sensors allowed the sample collection and gave initial assessment on reservoir fluids properties, as a result cost saving due to eliminating the need for additional Drill Stem Test (DST) run to confirm the fluid type. Sample and formation pressures has confirmed reservoir lateral continuity in the vicinity of the field. The reservoir developed as thick and blocky sandstone. Collected sample confirmed the low contamination levels. Continuous circulation mitigated sticking and potential well-control risks.

This is the first time in surrounding area, advanced optical sensors are used to aid LWD sampling and to finalize the fluid identification. The innovative technology allowed the collection of low contamination. The real-time in-situ fluid analysis measurement allowed critical decisions to be made real time, consequently reducing rig downtime. Reliable analysis of fluid type identification removed the need for additional run/service like DST etc.
Minimization of Ultra-High Temperature Filtration Loss for Water-Based Drilling Fluid with ß-Cyclodextrin Polymer Microspheres
Authors: Zhong Hanyi , Gao Xin , Qiu Zhengsong , Huang Weian , Liu Wenlei , Ma Jiaxin , Li Shusen
Published: December 2021

Due to the rapid degradation of conventional biopolymer or synthetic polymeric additives at high temperature (HT) or ultra-high temperatures (ultra-HT), effective control of water-based drilling fluid filtration in HT or Ultra-HT environment is still a great challenge in drilling operation.

β-cyclodextrin polymer microspheres (β-CPMs), generally using for drug release and waste water treatment, are evaluated as environmentally friendly ultra-HT filtration reducer. The impact of the microspheres on water-based drilling fluids’ properties including rheology and filtration prior to and after hot rolling at different temperatures ranging from 120 to 240°C was investigated. The high temperature and high pressure (HTHP) filtration properties of the microspheres compared to several commercial high temperature filtration reducers were conducted according to the API recommended procedures. The filtration controlling mechanism was analyzed from zeta potential measurement, particle size distribution measurement, and scanning electron microscope observation of filter cake.

The results indicated that the β-CPMs exhibited peculiar filtration behavior differently from conventional additives. When the hot rolling temperature was below 160℃, β-CPMs performed a 30% filtration reduction at 1 w/v% content in comparison with control sample. Once the hot rolling temperature was above 160℃, the capacity of filtration control was further improved with increasing temperatures. This is contrast with conventional filtration reducers that the filtration control capacity deteriorate with increasing temperatures. The microspheres still exhibited superior filtration control after exposure to 240℃. Furthermore, β-CPMs showed little effect on the drilling fluid's rheology. When the temperature was below 160℃, the filtration reduction was obtained by water absorption and swelling of β-CPMs. When the temperature was above 160℃, hydrothermal reaction occurred for β-CPMs. Numerous micro- and nano-sized carbon spheres formed, which bridge across micro and nanopores within filter cake and reduce the filter cake permeability effectively.

When the temperature was higher than 160℃, hydrothermal reaction occurs. Carbon spheres generated by the hydrothermal degradation of the β-CPMs, which are responsible for the effective filtration control. The hydrothermal reaction changes the adverse effect of high temperature into favorable improvement of filtration control, which provides a novel avenue for HT and ultra-HT filtration control. The β-CPMs show potential application in deep well drilling as environmental friendly and high temperature filtration reducers.
Heavy Oil Recovery by Alkaline-Cosolvent-Polymer Flood: A Multi-Scale Research Using CT Imaging
Authors: Su Hang , Zhou Fujian , Wang Lida , Wang Chuan , Kang Lixia , Yu Fuwei , Li Junjian
Published: December 2021

For reservoirs containing oil with a high total acid number, alkali-cosolvent-polymer (ACP) flood can potentially increase the oil recovery by its saponification effects. The enhanced oil recovery performance of ACP flood has been studied at core and reservoir scale in detail, however, the effect of ACP flood on residual oil saturation in the swept area still lacks enough research. Medical computed tomography (Medical-CT) scan and micro computed tomography (Micro-CT) scan are used in combination to visualize micro-scale flow and reveal the mechanisms of residual oil reduction during ACP flood. The heterogeneous cores containing two layers of different permeability are used for coreflood experiment to clarify the enhanced oil recovery (EOR) performance of ACP food in heterogeneous reservoirs. The oil saturation is monitored by Medical-CT. Then, two core samples are drilled in each core after flooding and the decrease of residual oil saturation caused by ACP flood is further quantified by Micro-CT imageing. Results show that ACP flood is 14.5% oil recovery higher than alkaline-cosolvent (AC) flood (68.9%) in high permeability layers, 17.9% higher than AC flood (26.3%) in low permeability layers. Compared with AC flood, ACP flood shows a more uniform displacement front, which implies that the injected polymer effectively weakened the viscosity fingering. Moreover, a method that can calculate the ratio of oil-water distribution in each pore is developed to establish the relationship between the residual oil saturation of each pore and its pore size, and reached the conclusion that they follow the power law correlation.
Integrated Semi-Supervised Clustering Method and Its Application in Rock-Typing in AA Reservoir
Authors: Ma Ruicheng , Hu Dandan , Deng Ya , Zhao Limin , Wang Shu
Published: December 2021

Rock-typing is complicated and critical for numerical simulation. Therefore, some researchers proposed several clustering methods to make classification automatic and convenient. However, traditional methods only focus in specific area such as lithofacies or petrophysical data instead of integrated clustering. Besides, all the clustering method are related to classification interval determined subjectively. Therefore, a new clustering method for rock-typing integrated different disciplines is critical for modelling and reservoir simulation.

In this paper, we proposed a novel semi-supervised clustering method integrated with data from different disciplines, which can divide rock type automatically and precisely. Considering AA reservoir is a porous carbonate reservoir with seldom fracture and vug, FZI (Flow Zone Indicator) and RQI (Reservoir Quality Index) is utilized as the corner stone of the clustering method after collection and plotting for porosity and permeability data for cores from AA reservoir. Then lithofacies, sedimentary facies and petrophysical data are applied as constraints to improve FZI method. Hamming distance and earth mover distance are imported to build integrated function for clustering method. Finally, based on output results of integrated clustering method from experimental data, grid properties of model in Petrel software are imported as the input parameter for further procession. Therefore, saturation region for numerical simulation built by rock-typing is constructed. The results show that new method could make classification accurately and easily. History matching results for watercut indicate that new saturation regions improve the numerical simulation performance.
Application of Tire Waste Material to Enhance the Properties of Saudi Class G Oil Well Cement
Authors: Ahmed Abdulmalek , Mahmoud Ahmed Abdulhamid , Elkatatny Salaheldin , Gajbhiye Rahul , Majed Abdulaziz Al
Published: December 2021

Cementing is an important operation for the integrity of the wellbore due to its role in providing several functions. To perform these functions, a high performance cement is required. Different types of additives and materials have been added to the cement slurry to improve its performance. Tire waste material is considered one of the greatest wastes globally. It is a dangerous material to the environment and human. Subsequently, it has been included in many industrial processes to reduce its hazards. This work evaluated the application of tire waste material in oil and gas industry to improve the properties of Saudi class G oil well cement.

Two cement slurries were formulated under high pressure and high temperature of 3000 psi and 292 °F, respectively. The first slurry was the base cement without tire waste and the second slurry contained the tire waste. The effect of using the two slurries on the cement properties such as density variation, compressive strength plastic viscosity, Poisson's ratio and porosity was evaluated.

The results showed that, when tire waste material was used, lower density variation was accomplished. Using tire waste was efficient to decrease the density variation to an extremely low proportion of 0.5%. Adding tire waste to the cement composition decreased its plastic viscosity by 53.1%. The tire waste cement sample had a higher Poisson's ratio than the base cement sample by 14.3%. Utilizing the tire waste improved the cement's compressive strength by 48.3%. The cement porosity was declined by 23.1% after adding the tire waste. Beside the property's enhancement in the cement, the application of tire waste has also an economical advantage, since it is inexpensive material which is influential in our daily life.
Pioneering Integrative Solution for Enhancing Wellbore Quality Thru the Application of Multiple Real Time Monitoring Services in Deviated and Lateral Sections in Deep Gas Wells
Authors: Barrera Edison , Nunez Andres , Atriby Kamal , Corona Mauricio , AlMahroos Mohamed , Garcia Arnott Evert Dorantes
Published: December 2021

In the Oil and Gas industry, there is a constant look for time and cost savings through performance enhancement and risk reduction. Not less important, wellbore quality becomes a crucial factor across target production intervals which enable safe and optimum completion operations in the well.

While the techniques to drill wells constantly evolve, technology is advancing at faster pace every year. The application of new tools and digital technologies is the step change from progessive growth to exponential increase in performance.

This paper contains a detailed description of a successful implementation of a combined integrated strategy, including the procedures established to maximize both; performance and wellbore quality in highly deviated and lateral horizontal sections in deep gas wells in a giant gas field in the Middle East. It describes the application of specific technologies that helped to improve wellbore quality and allowed corrections in Real Time.
Impediments to Refracturing Success in Shale Reservoirs
Authors: Kurison Clay
Published: December 2021

Stimulations in early horizontal wells in most shale plays are characterized by few and widely spaced perforation clusters, and low amounts of injected fracturing fluid and proppant. Low recovery from these wells has motivated refracturing although outcomes have been interpreted to range from successful to minimal impact based on operator specific evaluations. To tailor available technologies and improve quantification of upsides, there is need for mapping the spatial distribution of remaining resources and developing simpler but reliable analytical techniques. In this study, hydraulic fractures were assumed to be planar in a matrix with low porosity and ultra-low permeability. Consideration of natural fractures and their interaction with stimulation fluids led to addition of distributed fracture networks adjacent to the planar hydraulic fractures to define the composite fracture corridors. A sector model with the aforementioned architecture was used in reservoir simulation to investigate induced temporal and spatial drainage. These findings were used to explain the efficacy of widely used refracturing techniques and how post-refracturing reservoir response can be analyzed. Results from reservoir simulation showed remaining reserves were in the matrix between earlier placed hydraulic fractures aligned along initial perforation clusters, and beyond tips of hydraulic fractures. Upside from refracs could come from creation of new fractures in the matrix between earlier placed fractures and extension of tips of early fractures into virgin matrix. Assessment of these scenarios found the former to be optimal although depletion and existing perforations would limit the stimulation efficiency of new perforations. The second scenario would require large volumes of fracturing fluid to re-initiate fracture propagation. Yet this could trigger interference with offsets or affect drilling and stimulation of planned wells in adjacent acreage. For treatment efficiency, re-casing horizontal wells with competent liners and use of coiled tubing with straddle packers appears a better solution for bypassing old perforations. For the near wellbore and far field, re-stimulating new perforations at low injection rates could allow extension of fractures in virgin matrix surrounded by depleted strata. Real-time surveillance would be essential for mapping flow paths of refracturing fluid. For assessment of refracturing, actual and simulated flow exhibited persistent linear flow (PLF) that could be matched by Arps hyperbolic equation with a b value of 2. Incorporation of a novel fracture geometry factor (FGF) yielded an Arps-based equation that was tested on North American shale refracturing cases that often use post-treatment peak rate and wellhead pressure as measures of success. This study identified factors hindering the success of refracturing and proposed a modified Arps hyperbolic equation to analyze refracturing production data.
Barite-Free Non-Aqueous Drill-In Fluid System Maximizes Productivity in High Temperature Wells
Authors: Chimara Godwin , Amer Wael , L'Hostis Stephane , Leslie Philip
Published: December 2021

Minimizing formation damage is vital for maximizing productivity when an openhole (slotted liner) completion strategy is used, and it is particularly challenging in high temperature wells with bottomhole static temperature approaching 190°C (374°F). A barite-weighted fluid system for such high temperature wells was identified as unsuitable due to lack of ability to remediate via acid treatment. This paper discusses how a customized barite-free non-aqueous drill-in fluid system was used to successfully achieve productivity objectives for three such wells.

Based on reservoir and well data provided, a 1.15 to 1.20 sg (9.60 to 10.0 lbm/gal) barite-free, non-aqueous drill-in fluid system was designed using a high density calcium chloride/calcium bromide brine as the internal phase to compensate for the absence of barite as a weighting agent. An engineered acid-soluble bridging package was included to protect the reservoir from excess filtrate invasion and allow for potential remediation by acid treatment at a later stage. The fluid system was subjected to formation response testing, and the results obtained proved satisfactory, confirming the fluid system was suited for drilling the reservoir. A similar solids-free system using higher density brine as the internal phase, was also formulated. This was spotted in the open hole once drilling was completed to help eliminate any potential for solids settling before running the slotted liner.

Three wells were successfully drilled and completed. The barite-free system remained stable, allowed for trouble-free fluids-handling and drilling operations, and performed as expected. To aid in minimizing fluid invasion into the reservoir, onsite particle size distribution (PSD) measurements were performed in order to optimize bridging material additions while drilling and enhance efficiency in managing the solids control system. Because of the extremely high bottomhole temperature, a mud cooler was installed to help control the flowline temperature below 60°C (140°F); this helped maintain fluid stability and preserve functionality of downhole tools in this hostile environment. The solids-free system was successfully spotted in the open hole after drilling the section before well completion. This eliminated any settling potential and reduced flowback of solids during production. The recorded productivity of these wells met expectations.
High Performance Computing on the Cloud Successfully Deployed for 3D Geosteering and Reservoir Mapping While Drilling
Authors: Salim Diogo , Thiel Michael , Nesttun Øyen Beate , Bakti Tan Kong , Denichou Jean-Michel , Krissetiawati Wibowo Vera , Zhang Desheng , Harms Kent , Etchebes Marie , Antonsen Frank , De Oliveira Maria Emilia
Published: December 2021

The successful drilling of horizontal wells targeting reservoir zones of interest can be challenged by uncertainties in geological interpretation, identification of structure, and properties of reservoirs and fluid distribution. Optimizing the well placement of high-angle wells in order to intercept the sweet spots is crucial for the total hydrocarbon recovery in any development field. Thus, the geosteering domain was implemented to provide in real time a reservoir mapping characterization together with directional control to achieve the key performance objectives. In the past, many innovative technologies have been introduced in geosteering discipline, among them lately the deep EM directional resistivity tool that provides 1D formation resistivity mapping while drilling.

However, despite the fact of delivering a multilayer mapping of the reservoir structure up to tens of meters away from wellbore, the real-time interpretation can be limited by this type of inversion. Since it is a 1D approach, these inversions map resistive boundaries on the vertical axis and assume infinite extend in all other directions. Consequently, in a complex geological setting, 1D approximation may fall short of properly describing the reservoir structure.

This communication describes how the introduction of the 2D azimuthal resistivity inversions while drilling was conducted and details the various innovations required in the domains of downhole logging while drilling (LWD) measurements transmission in addition to adaptation of inversion methodology for real-time deployment, mainly through the use of high-performance cloud computing. The final enablement was the execution of automated workflows to process and deliver these advanced inversions into an integrated 3D geomodelling software within the turnaround time of drilling operations.

This novel technology provides, while drilling, a better understanding of the 3D geological environment and fluid distribution with a deep depth of investigation, as well as the required information to make support for geosteering decisions for optimal well positioning. Initial field deployments were successfully conducted in horizontal wells, and three examples are presented here. Those real cases, executed with wire-drilled-pipe or mud-pulse telemetries, demonstrated the benefits of integrating 2D azimuthal inversions into the current geosteering workflow to provide a complete 3D structural understanding of the reservoir while drilling. This communication documents in detail how such an approach led to operational efficiency improvements in the form of 3D reservoir mapping in real-time, supporting a strategic change in the original well to turn toward the sweet spot, which was located sideways from the planned trajectory.
Intelligent Predictor for Polymer Viscosity to Enhance Support for EOR Processes
Authors: Khan Mohammad Rasheed , Kalam Shams , Asad Abdul , Khan Rizwan Ahmed , Kamal Muhammad Shahzad
Published: December 2021

Research into the use of polymers for enhanced oil recovery (EOR) processes has been going on for more than 6 decades and is now classified as a techno-commercially viable option. A comprehensive evaluation of the polymer's rheology is pivotal to the success of any polymer EOR process. Laboratory-based evaluation is critical to EOR success; however, it is also a time/capital consuming process. Consequently, any tool which can aid in optimizing lab tests design can bring in great value. Accordingly, in this study a novel predictive correlation for viscosity estimation of commonly used "FP 3330S" EOR polymer is presented through use of cutting-edge machine learning neural networks.

Mathematical equation for polymer viscosity is developed using machine learning algorithms as a function of polymer concentration, NaCl concentration, and Ca2+ concentration. The measured input data was collected from the literature and sub-divided into training and test sets. A wide-ranging optimization was performed to select the best parameters for the neural network which includes the number of neurons, neuron layers, activation functions between multiple layers, weights, and bias. Furthermore, the Levenberg-Marquardt back-propagation algorithm was utilized to train the model. Finally, measured and estimated viscosities were compared based on error-analysis.

Novel correlation is developed for the polymer that can be used in predictive mode. This established correlation can predict polymer viscosity when applied to the test dataset and outperforms other published models with average error in the range of 3-5% and coefficient of determination in excess of 0.95. Moreover, it is shown that neural networks are faster and relatively better than other machine learning algorithms explored in this study. The proposed correlation can map non-linear relationships between polymer viscosity and other rheological parameters such as molecular weight, polymer concentration, and cation concentration of polymer solution. Lastly, through machine learning validation approach, it was possible to examine feasibility of the proposed models which is not done by traditional empirical equations.
A Case Study Using Integrated Multi Disciplinary Approach to Model Microseismic Events During Stimulation
Authors: Izadi Ghazal , Barton Colleen , Roux Pierre-Francois , Llobet Tebis , Pessoa Thiago , McGlynn Ian , Friedrichs Meagan , Fernandez Americo , Mathieu Jean-Philippe , Vinchon Matthieu , Onaisi Atef
Published: December 2021

For tight reservoirs where hydraulic fracturing is required to enable sufficient fluid mobility for economic production, it is critical to understand the placement of induced fractures, their connectivity, extent, and interaction with natural fractures within the system. Hydraulic fracture initiation and propagation mechanisms are greatly influenced by the effect of the stress state, rock fabric and pre-existing features (e.g. natural fractures, faults, weak bedding/laminations). A pre-existing natural fracture system can dictate the mode, orientation and size of the hydraulic fracture network. A better understanding of the fracture growth phenomena will enhance productivity and also reduce the environmental footprint as less fractures can be created in a much more efficient way. Assessing the role of natural fractures and their interaction with hydraulic fractures in order to account for them in the hydraulic fracture model is achieved by leveraging microseismicity. In this study, we have used a combination of borehole and surface microseismic monitoring to get high vertical resolution locations and source mechanisms. 3D numerical modelling of hydraulic fracturing in complex geological conditions to predict fracture propagation is essential. 3D hydraulic fracturing simulation includes modelling capabilities of stimulation parameters, true 3D fracture propagation with near wellbore 3D complexity including a coupled DFN and the associated microseismic event generation capability. A 3D hydraulic fracture model was developed and validated by matching model predictions to microseismic observations. Microseismic source mechanisms are leveraged to determine the location and geometry of pre-existing features. In this study, we simulate a DFN based on the recorded seismicity of multi stage hydraulic fractures in a horizontal well. The advanced 3D hydraulic fracture modelling software can integrate effectively and efficiently data from a variety of multi-disciplinary sources and scales to create a subsurface characterization of the unconventional reservoir. By incorporating data from 3D seismic, LWD/wireline, core, completion/stimulation monitoring, and production, the software generates a holistic reservoir model embedded in a modular, multi-physics software platform of coupled numerical solvers that capture the fundamental physics of the processes being modelled. This study illustrates the importance of a powerful software tool that captures the necessary physics of stimulation to predict the effects of various completion designs and thereby ensure the most accurate representation of an unconventional reservoir response to a stimulation treatment.