bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Study of Low Salinity Water Flooding: The Effect of Polar Organic Components in Low-Permeable Sandstone Reservoir
Authors: Al-Saedi Hasan N. , Qubian Ali , Al-Bazzaz Waleed , Flori Ralph 
Published: January 2020

Low salinity (LS) water flooding in the sandstone reservoir is of pronounced interest of the prospective for improved oil recovery. In this study, laboratory experiments in low-permeable sandstone core plugs saturated with various crude oil containing different acid numbers were presented. Several low-permeable sandstone cores (1-3 mD) were taken from Bartlesville Sandstone Reservoir from Eastern Kansas were successively flooded with seawater and different LS water. The reservoir cores were cleaned and saturated with formation water (FW) and then aged in three kinds of crude oil (different acid numbers) for six weeks at 90°C. To evaluate LS water in the low-permeable reservoir core plugs, core flooding tests were performed. Contact angle and spontaneous imbibition tests were also carried out.
The results obtained from LS water flooding showed that an improvement in oil recovery up to 12% of the original oil in place when the acid number (AN) and core permeability were low. The water wetness, and in turn, the oil recovery reduced with increased crude oil's AN and as the permeability increased. The contact angle and spontaneous imbibition tests confirmed the appropriate wettability change is attainable with LS water flooding. The results were deliberated in relation to wettability change processes by LS Water.
Design, Optimization and Application of the Closed Blender for CO2 Waterless Fracturing
Authors: Yang Qinghai , Meng Siwei , Yu Chuan , Fu Tao , Chen Shi 
Published: January 2020

The CO2 waterless fracturing uses the liquid CO2 to replace water as the fracturing fluid in reservoir stimulation. The continuity and reliability of the blender are key factors determining the performance of the waterless fracturing operation.
This paper proposed a novel closed blender, which introduces a vertical tanker for the operation, instead of a horizontal one. This modification can reduce the footprint and also effectively suppress the CO2 gasification. The proppant supply is controlled collectively by the short auger rotation at the bottom of the blender, the opening adjustment of the proppant dosing butterfly damper and the liquid supplement rate of the blender tanker. Such proppant transportation combing the mechanical and non-mechanical approaches is able to ensure the proppant transportation stability and meanwhile greatly lower the maintenance cost.
The effective volume of the blender is 27 m3, and the maximum output flow rate is 8 m3/min, with a rated pressure of 3 MPa and a minimum operation temperature of −40°C. On the basis of the ground testing and field practice, the overall correlation among the liquid input, output, butterfly damper opening degree and the proppant transportation intensity has been well established, which facilitates the control of the system pressure and proppant supply. So far, the blender has been stably operated at the CO2 waterless fracturing operation site for over 20 times. The pump rate can be up to 8 m3/min for a single operation, the sand transportation reaches 23 m3, and the liquid consumption amounts to 860m3. The proppant transportation is seen with good stability. The reservoir simulation performance is considerable, and the post-treatment production of the CO2 waterless fracturing is 2.4 times that of the conventional hydraulic fracturing with an identical treatment scale.
The field practice suggests that the developed closed blender combines the advantages of both the vertical and horizontal blenders, and ensures the successful implementation of the CO2 waterless fracturing operation.
Paradigm Shift in Smartwater Simulation Methods
Authors: Alshakhs Mohammed J. , BuKhamseen Nader Y. , AlGarni Saad A. 
Published: January 2020

Laboratory studies of Smartwater flood have demonstrated its impact on increasing oil recovery. Smartwater refers to the injection water that is subjected to alteration of total salinity or ionic composition. Several simulation techniques are carried out to match experimental observations. The objective of the paper is to critically examine and assess the simulation practices in the industry.
Simulation strives to depict the production profiles along with the primary chemical and physical processes that take place. The simulation studies in the literature were surveyed and classified into categories based on the simulation approach. The capability of the simulation techniques to mimic the oil recovery mechanisms were evaluated. In addition, we explored the operational advantages and limitations of each technique.
Most of the simulation techniques estimate the increase in oil recovery mainly from the changes of the relative permeability curves and residual oil saturation. A correlation is often established between these data sets and certain traceable factors such as the total salinity. We identified two adopted modeling categories: single component, and mechanistic reaction models. The single component model is the most commonly used because it requires few additional input data with relatively lower uncertainty. The mechanistic reaction model is a new emerging method that accounts for oil-water-rock interactions. The main limitation of this method is that most simulators are not equipped with chemical reaction functions. In such a case, an external simulation package is required to simulate the reactions. The paper concludes that themechanistic reaction model is an excellent options to replicate the micro-scale processes. Integrating the conventional simulators with chemical reaction functions is required to improve the simulation efficiency and accuracy.
There is a gap in the literature between investigating oil recovery mechanisms associated with Smartwater flood and reflecting them in the conventional fluid-flow simulation. To motivate more efforts and bridge the gap, this review provides insights of the recent advancements in the simulation techniques and addresses the expectations from each technique.
Reliable Models for Determining the Pressure-Volume-Temperature PVT Properties Using Artificial Intelligence Technique
Authors: Hassan Amjed , Abdulraheem Abdulazeez , Mohamed Abdelmjeed 
Published: January 2020

In petroleum industry, PVT properties are very important in predicting the performance of oil and gas reservoirs. Several laboratory measurements are used to determine these properties at reservoir condition. The PVT measurements are costly and time-consuming; therefore, numerous correlations were developed to predict the PVT properties based on primary inputs such as reservoir pressure, temperature and hydrocarbon gravities. However, significant deviations are reported between the actual values and the predicted results. The aim of this paper is to present reliable and rigorous models to determine the PVT properties using artificial intelligence technique.
Artificial neural network was utilized to develop the pressure-volume-temperature models. The proposed models estimate the PVT properties based on the pressure, temperature, oil and gas densities, and the solution gas-oil ratio. The developed models determine the bubble point pressure, formation volume factor and solution gas-oil ratio. Total of 250 data sets were used to develop and evaluate the model reliability. Average absolute percentage error (AAPE) and coefficient of determination (R-value) were used to evaluate the model reliability.
The new models are simple, accurate and easy to use when compared with the existing empirical equations. The obtained results showed that the developed models are able to determine the PVT properties with an average absolute error of around 7.7%. The intelligence models developed in this study outperform the popular PVT equations such as Standing and Al-Marhoun correlations. Average errors of 6.8% and 11.7% were obtained for the developed model and the popular PVT correlations, respectively. Therefore, the developed models can highly increase the quality of production management by providing an accurate estimation for the PVT properties. Also, the presented models can significantly reduce the time and cost required for conducting the PVT measurements.
Application of Combined Streamline Based Reduced-Physics Surrogate and Response Surface Method for Field Development Optimization
Authors: Tanaka Shusei , Onishi Tsubasa , Kam Dongjae , Dehghani Kaveh , Wen Xian-Huan 
Published: January 2020

Model-based field development optimization typically requires a large number of simulations. Consequently, this process may face challenges as model size and complexities increase. The objective of this paper is to apply a reduced-physics model with response surface approach for replacing full field simulation runs to reduce the time and resources required during a search for optimal solutions.
The streamline technique is used to develop a reduced-physics model in this study. There are number of previous studies that demonstrated the use of streamlines for production optimization (e.g. well placement and/or rate allocation optimization). In a recent work (SPE 187298), an approximate equation was formulated to estimate the expected economic value based on streamlines and was applied into rate allocation optimization of a given well pattern. Our approach is to use this formulation to improve the efficiency of field development optimization by potentially screening out poor performing development designs without performing full simulations.
The streamline-based surrogate model workflow was first applied and validated using a synthetic SPE-10 case study. The workflow was then applied to the Olympus field waterflood study. The study goal is to maximize the economic value by optimizing the well count, injector and producer locations, and completion design. The validation performed using random field development designs provided a rank correlation coefficient of 0.92 between the NPV values of full field simulations and streamline-based approximation from the Olympus field application. The streamline-surrogate model was then adopted with an optimization workflow (Genetic Algorithm) and response surface method with 2-stage approach. First, Genetic Algorithm (GA) optimization was performed using the streamline-surrogate as an initial stage to screen out suboptimal field development design. Then, a second GA optimization was performed using full simulations coupled with the response surface method, starting with results from the first stage. Response surfaces that were developed using samples through GA improved the process of screening poor economic cases at later stages, as the predictability of solution improved with more training.
We demonstrated that the streamline-based surrogate formulation combined with the response surface approach will improve the optimization process of field development scenarios. The applicability of using the response surface approach by itself is limited for field applications due to the large number of simulations required for training and the risk of convergence at local minima. Multiple tests from the Olympus field development application demonstrated that the sequential combination of streamline-based surrogate formulation with response surface method had the best performance.
Produced Water Quality Impact on Injection Performance: Predicting Injectivity Decline for Waterflood Design
Authors: Rossini Stefano , Roppoli Giulia , Mariotti Pamela , Renna Simona , Manotti Matteo , Viareggio Alberto , Biassoni Laura 
Published: January 2020

The management of produced water has become a main issue in petroleum industry due to the huge quantities to be dealt with. Produced Water Re-Injection (PWRI) allows combining disposal with Improved Oil Recovery (IOR) opportunities. Nevertheless, PWRI could damage the formation, eventually compromising field performances. Well impairment is a complex phenomenon depending on several aspects. This paper focuses on the impact of Oil In Water (OIW) in injectivity performance, validating lab predictions and field evidences.
A comprehensive review of field cases has been carried out, analyzing injection well performance with Production Data Analysis (PDA) tools together with water quality data. On a selection of field cases where OIW has been identified as main impairment reason, a workflow was established performing core flooding experiments to measure experimental loss of permeability with different content of OIW. Obtained results have been integrated by bibliographic research.
Field evidences showed a direct relationship between permeability reduction and hydrocarbon content; moreover, injectivity impairment measured on field data has been found to be comparable (same order of magnitude) with the permeability reduction measured on core flooding. Combining all data together, a common trend of injectivity reduction vs. OIW content has been extrapolated passing through the definition of Injectivity Index (II).
New water injection concept depends on several elements that could affect the overall project value, water quality requirements is one of them. Water quality rule of thumb can be found in literature, but each project basis of design is different. The proposed approach allows to preliminarily quantifying the expected well impairment as function of water quality. It can be used as a first tool to dimension treatment facilities in function of the reduction of injectivity that we can accept for any given reservoir. Obtained trend is representative of a subset of real field cases, where OIW content is the main impacting parameter on PWRI well damage.
The Most Reliable and Cost Effective Advanced Sulphur Recovery Technologies for Saudi Arabia and the Middle Eastern Region
Authors: Siu Jerry H. , Vo Ngo Bao C. , Chow Thomas K. 
Published: January 2020

Gas Processing and Refining facilities in Saudi Arabia and the Middle East region are generally embracing the following design features and operation requirements for sulphur recovery facilities: Operating under hot and dry climate; lack of processing, cooling and utility water; requiring complete destruction of BTEX and Ammonia contained in gas feeds; gas feeds with higher and higher sulphur contents; requirements for expanded processing capacity in terms of sulphur contents, gas volumetric flow rates and higher overall sulphur recovery efficiency; and compliance of more stringent HSE requirements. Technologies associated with Sulphur Recovery facilities to be discussed in this paper are targeted to address these specific design features, operation and HSE requirements.
In this paper, Fluor will discuss four reliable, cost effective and commercially proven technologies as well as their respective most current improvements for new and existing sulphur recovery facility applications. These include Fluor/GAA COPE™ Oxygen Enrichment Technology, Fluor’s proprietary Hydrogenation-Amine Tail Gas Treating Technology, Fluor’s patented Low Energy Direct Contact Condenser Technology, and Fluor/GAA’s D’GAASS™ Liquid Sulphur Degassing Technology.
Advantages of each of these technologies over competing technolgies in the industry will be discussed in this paper. Their operating experience and lessons learned associated with design and operation will be addressed in terms of what improvements in design and operation are being implemented into new design practices. Comparisons in estimated CAPEX and OPEX between the original design and the new improved design will be presented for each of these technologies in the form of Case Studies.
Open Fracture Detection from the New-Generation Oil-Based Microresistivity Image Logs: A Case Study from the Bashijiqike Sandstone, Tarim Basin of China
Authors: Liu ChangXin , Wang XiaoYun , Zhang XinLei , Mo Jing , Wang Dali , Wei Guo , Pan Feng 
Published: January 2020

The Cretaceous Bashijiqike formation is the main target of the tight gas production of Tarim Basin. Low porosity and low permeability were validated by core data. However, mud loss of 46.6 m3 occurred near high angle open fractures, and gas production and oil production has been tested in high angle open fracture section. It suggests that high angle open fractures can cause mud loss and they also have a positive effect on tight gas reservoirs.
Open fractures are the key point to the tight gas reservoirs of Tarim Basin. However, it was impossible to identify open fracture and cemented fracture through conventional logs and seismic data. Ultrasonic image logs and oil-based electrical image logs make it possible to oil-based mud well. The new-generation oil-based microresistivity image logs provide high resolution images with high borehole coverage. It improves the image quality of the previous oil-based microimager logs (OBI) in oil-based mud well.
A total of 39 open fractures and 33 cemented fractures in a vertical well were recognized from the new-generation oil-based microresistivity image logs. Core observation for this vertical well also validated the presence of both cemented fracture and open fracture. In addition, ultrasonic image logs were acquired in this vertical well. According to the ultrasonic image features, it only responds to large open fracture with bigger aperture and has limitation to relatively small-scale open fractures with smaller aperture. The new-generation oil-based microresistivity image logs complement the limitation of the ultrasonic logs.
The case study presents how to identify different kind of fractures by the high-resolution oil-based microresistivity image logs in oil-based mud well, especially open fracture detection. As the main gas migration pathway, the open fractures were important to tight sandstone gas reservoir quality in the Bashijiqike formation of the Tarim Basin. Better understanding of the fracture attributes was instructive for fracturing stimulation design of tight gas reservoir as well.
Augmented AI Framework for Well Performance Prediction and Opportunity Identification in Unconventional Reservoirs
Authors: Darabi Hamed , Zhai Xiang , Kianinejad Amir , Ma Zheren , Castineira David , Suicmez Vural Sander , Toronyi Robert 
Published: January 2020

Abstract
Many important business decisions and planning in unconventional reservoirs rely on a reliable forecast on well performance. Common practices like statistical type curves, analytical methods, and numerical simulation are not well suited to incorporate all the complexities involving rock/fluid properties, geological parameters, artificial lift systems, well and completion designs, etc. In this work, we introduce a novel "Augmented AI" (Artificial Intelligence) workflow for reliable forecasting of unconventional well performance and show its impact on decision making. Augmented AI represents smart integration of artificial intelligence and domain knowledge. In the application of well performance forecast, a smart DCA algorithm automatically estimates the short- and long-term performance of the historical wells; a spectrum of well attributes are aggregated/transformed with the consideration of uncertainty and robustness for training and prediction. Boosting and bootstrap tree-based models are ensembled to maximize the model generalization capability. In contrast to the commonly seen black-box modeling practices, the factor-specific impacts are deconvoluted, allowing for validation of the underlying physics. Furthermore, this gives guidelines for future well planning and completion designs. A case study is presented, where the workflow is implemented. Multi-disciplinary data (logs, completions, maps, fluid properties, etc.) from thousands of wells were integrated. During the feature engineering step, raw data was converted to a set of meaningful parameters leveraging the domain knowledge. As an example, some of the features were combined, some were transformed, and others were normalized. Then a machine learning model was created using an ensemble approach. The models showed a good model accuracy on the training, testing, and validation dataset. Leveraging the predictive model, thousands of field development opportunities including new vertical wells, new horizontal wells, recompletions, and completion optimization were identified that resulted in increased production, increased reserves, and improved capital efficiency. Using the model explanation techniques, the impact of various parameters on the well performance was quantified that resulted in best practices for future drilling and completion design.
Data Analytics Approach to Frac Hit Characterization in Unconventional Plays: Application to Williston Basin
Authors: Abivin Patrice , Vidma Konstantin , Xu Tao , Boumessouer Wissam , Bailhy Jason , Ejofodomi Efe , Sharma Amit , Menasria Samir , Makarychev-Mikhailov Sergey 
Published: January 2020

Infill drilling consists of adding horizontal wells between existing wells to optimize drainage in high-value acreage. New wells are sometimes drilled as close as 250 ft to producing or depleted wells. Fracturing the new wells creates a high risk of fracture-driven interactions (FDI). This paper describes a methodology to characterize well interference on production in unconventional basins and the impact of mitigation technologies.
Data mining, correlations, and statistical tools were developed to extract and analyze a large commercial production database covering major plays in the US. First, cloud-based algorithms were developed to identify and characterize infill wells based on coordinates, well deviations, production dates, and an adjustable radius of interference. Second, monitoring algorithms automatically captured and analyzed abrupt changes in normalized production of infill wells and neighboring wells at the time of infill well stimulation. Finally, the effect on production of both parent and child is immediately displayed on a user-friendly user interface for further visualization and interpretation.
The method was successfully applied to areas experiencing high infill drilling in major basins such as the Williston basin. Results show that production data correlate with historical changes in infill drilling density and fracturing job volumes (proppant and fluid). The production of child wells is then compared to that of their closest parent, which shows some decline as a function of the distance between wells. The systematic workflow also identifies if the basin is prone to positive fracture hits or if there is a significant decrease in the production of existing (parent) wells. The use and impact of diversion technologies as a well interference mitigation method is also studied.
These results give important insights into the effect of field development strategies on well interference and enable recommendations related to well spacing, fracturing designs, and use of fracture geometry control technologies to optimize future well and field development. Production data analysis clearly shows a beneficial impact of both near-wellbore and far-field diversion technologies on production.