bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of Big Data Analytics Based Ensemble Estimator Module for the Real-Time Prediction of Reservoir Recovery Factor
Authors: Tewari Saurabh , Dwivedi U. D. , Shiblee Mohammed 
Published: March 2019

Abstract
Production of oil & gas depends upon the recoverable amount of hydrocarbon existing beneath the underlying reservoir. Reservoir recovery factor provides of the production potential of ‘proven reservoirs’ which helps the planning of field development and production. Estimation of reservoir recovery factor, with a good degree of accuracy, is still a challenging task for engineers due to the high level of uncertainty, large inexactness, noise, and high dimensionality associated with reservoir measurements. In this paper, we propose a big data-driven ‘ensemble estimator’ (E2) module, comprising of wavelet associated ensemble models for the estimation of reservoir recovery factor. All the ensemble models in E2 were trained on big reservoir data and tested with unknown reservoir data samples obtained from U.S.A. oil & gas fields. Bagging and Random forest ensembles have been utilized to correlate several reservoir properties with reservoir recovery factor. Further, E2 utilizes Relief algorithm to understand the significance of reservoir properties effecting the recovery factor of a reservoir. The proposed E2 module has provided impressive estimation results for the determination of reservoir recovery factor with minimum prediction error. Random forest has given the highest coefficient of correlation (R2=0.9592) and minimum estimation errors viz. mean absolute error (MAE=0.0234) and root mean square error (RMSE=0.0687). The performance of the proposed E2 module was also compared with conventional estimators viz. Radial basis function, Multilayer perceptron, Regression tree and Support vector regression. The experimental results have demonstrated the supremacy of E2 over conventional learners for the estimation of reservoir recovery factor.
Application of Automated Machine Learning for Multi-Variate Prediction of Well Production
Authors: Maucec Marko , Garni Saad 
Published: March 2019

Abstract
Performance evaluations of oil and gas assets are crucial for continuously improving operational efficiency in the mainstream petroleum industry. The success of such evaluations is largely driven by the analysis of the data accumulated during the asset's operational cycle. Usually, the amount of data stored in the databases dramatically exceeds the ability to approach the analysis with traditional spreadsheet-based tools or linear modeling. In this study we use data mining with multivariate predictive analytics and monetize on the value of data by transforming the inferred information into knowledge and further into rigorous business decisions.
With the expansion of the Digital Oil Field and transformation into the 4th Industrial Revolution, the oil and gas industry is acquiring tremendous amounts of data that come from disparate sources in a variety of origins, time scales, structures and quality. The underlying variable root-cause relationships are highly non-linear and non-intuitive, and simplistic linear regression methods are suboptimal. We approach the challenge by developing a data-driven workflow that integrates components of artificial intelligence, machine learning and pattern recognition to enhance quantitative understanding of complex data.
The sanitized aggregated data set combines 470 horizontal wells, covering 15 numerical (e.g., stimulation interval length, production rates) and categorical (e.g., target zone, proppant type) predictors and the total produced BOE, as the response variable. The objective is to predict an optimal set of variables that maximize the production. We utilize an integrated analytics platform that enables a variety of sophisticated statistical operations on large-scale data: a) comprehensive data QA/QC for outliers, consistency and missing entries; b) Exploratory Data Analysis and visualization; c) feature selection, screening and ranking; d) building and training of multiple machine learning (ML) models for multi-variate regression (e.g. generalized linear model, deep learning, decision tree, random forest and gradient boosted machine); and e) response optimization of an identified "best-performing" ML model for highest prediction accuracy.
Our study introduces the initiative to establish concepts best practices for predictive and prescriptive analytics in domains of reservoir simulation, description and asset management. Given the unique volume and information richness of operational data, acquired over decades of production history, the anticipated applications of predictive analytics could expand to drilling optimization, smart data aggregation, well stimulation and equipment maintenance.
Enhancing the Near-Surface Image Using Duplex-Wave Reverse Time Migration
Authors: Sindi Ghada , Alkhalifah Tariq , Fei Tong , Luo Yi 
Published: March 2019

Abstract
Reverse time migration (RTM) involves zero-lag cross-correlation of forward extrapolated source function wavefields and backward extrapolated receiver wavefields. For a near surface with complex structures and velocity anomalies, forward propagating the source wavelet generates wavefields containing reflections, near-surface multiples, and scattered direct arrivals. The wavefields are recorded as upgoing arrivals contaminated by the same reflections, near-surface multiples, and scattered signals, which can be critical for imaging near-surface structures and scatterers.
Here, we develop a new depth migration, duplex reverse time migration (DRTM) technique to improve imaging of complex near-surface structures. DRTM uses the direct arrival as a source to forward propagate and generate source wavefields, and reversely extrapolated recorded data in a zero-lag cross-correlation imaging condition to generate the final section. The interaction between the data components during cross- correlation can use primaries and multiples to image the near-surface structure correctly. Cross-talk artifacts may exist, but they are comparatively weak.
DRTM is demonstrated on both synthetic and field data examples showing an enhanced image in areas with complex near-surface structures compared to conventional RTM imaging methods. The new algorithm can significantly enhance shallow imaging without additional computation costs compared with conventional RTM. It can produce an image with higher resolution and signal-to-noise (S/N) ratio by replacing the source wavelet with the recorded direct arrivals, which include near-surface information necessary to boost the image in areas with near-surface complexity. Since the direct arrivals are one of the most energetic events recorded, the resultant image is typically of high S/N. The wave can also illuminate shallow zones better than primaries in marine environments.
Evaluation of the Performance of a New Biodegradable AA-LDHI in Cyclopentane Hydrate and CH4/C3H8 Gas Hydrate Systems
Authors: Henry Delroisse , Gilles Barreto , Jean-Philippe Torré , Christophe Dicharry , Philippe Glénat 
Published: March 2019

Abstract
The main objective of this work is to evaluate the performance of a readily biodegradable anti- agglomerant, called AA-LDHI, not harmful for the environment following the European legislation. It was tested in cyclopentane (CP) hydrate and methane (CH4)/propane (C3H8) hydrate systems. The performance of AA-LDHI was first tested in a batch reactor by torque measurements with the two hydrate systems in oil-dominated conditions (70 vol%). The experiments were performed for a subcooling of 6 °C for the CP-hydrate and up to 17 °C for the gas hydrate. The impact of AA-LDHI on hydrate growth pattern and on hydrate crystal morphology were investigated by microscopic observations at the water/CP interface for the CP-hydrate system, and at a water/(n-octane + CH4/C3H8) interface for the gas hydrate system. Then, AA-LDHI was evaluated in a semi-industrial flow loop.
Without surfactant, the hydrate formed a polycrystalline shell at the water/oil interface. With AA- LDHI, large conical hydrate crystals with the vertex pointing to the aqueous phase grew at the interface before sinking in the aqueous phase. The results obtained with both CP and gas hydrates are consistent amongst themselves.
In the batch reactor experiments without surfactant, hydrate crystallization led to a significant increase in the torque value and finally to the blockage of the agitator. With AA-LDHI, the torque remained almost constant at the baseline showing that this AA presented good anti-agglomeration performance. The microscopic observation in a CP phase of CP-hydrate particles formed in the reactor showed large water-wettable particles (about 400 μm) gathered in clusters but not agglomerated.
AA performance of AA-LDHI was also evaluated in a semi-industrial flow loop (1-inch diameter & 35.6 m total length) in similar conditions with CH4/C3H8 mix gas but using real condensate. This experiment proved that the AA-LHDI is fully efficient in transporting safely hydrates.
Optimization of Hydraulic Fracture Spacing Through the Investigation of Stress Shadowing and Reservoir Lateral Heterogeneity
Authors: Alrashed Ahmed , Miskimins Jennifer , Tura Ali 
Published: March 2019

Abstract
Monitoring of multi-stage hydraulic fractures in unconventional reservoirs has shown that some fractures are more effective and productive than others. Stress shadowing, in addition to reservoir lateral heterogeneity, are two potential factors behind this phenomenon. The focus of this study is to find the optimum hydraulic fracture spacing that aims to reduce the stress shadowing effect and ensure placement of hydraulic fractures in the best quality reservoir rock along the horizontal lateral.
A base hydraulic fracture model was created for a well in the Eagle Ford reservoir. Fiber optic distributed acoustic sensing (DAS) data were analyzed to find the individual perforation cluster contribution to production based on the total proppant placed in each cluster. The modeled well cluster contribution and production data were then matched with actual data. Reservoir and geomechanical properties for certain stages of the horizontal wellbore were altered from the base model to address the effect of rock quality lateral variations. Four scenarios of 57 ft, 76 ft, 100 ft, and 142 ft spacing between perforation clusters were investigated to address the effect of stress shadowing.
The sensitized reservoir and geomechanical properties include matrix permeability, Poisson's ratio, and Biot's coefficient. Increasing the matrix permeability from a base value of 0.2 ?D to 2 ?D caused the flowing fracture lengths to increase by 69%, 68%, and 48% in the heel, middle, and toe clusters, respectively. Stages with higher Poisson's ratio of 0.33, compared to a base value of 0.28, created larger flowing fracture lengths by 32% and 41% in the heel and middle clusters. Altering Biot's coefficient resulted in the same effect on flowing fracture lengths as altering Poisson's ratio. Overall, the rate of increase in flowing fracture lengths as a response to changing these properties was found to be more pronounced in the heel and middle clusters but less evident in the toe clusters. As for the cluster spacing scenarios, simulations showed that tighter spacing scenarios yielded a larger fracture network volume due to the higher number of clusters. However, these created fractures were less conductive than the ones created with wider spacing scenarios due to the stress shadowing effects. Production runs showed that scenarios with more accessed reservoir volume via more perforation clusters yielded a larger cumulative production over a 30-year simulation period.
A New Approach to Simulate Low Salinity Water Flooding in Carbonate Reservoir
Authors: Aljaberi Abdulla , Sohrabi Mehran 
Published: March 2019

Abstract
Low salinity water flooding (LSWF) as an enhanced oil recovery method (EOR) has attracted increased attention from oil companies due to its numerous benefits and advantages. It has been confirmed in several studies and laboratory experiments that LSWF has improved oil recovery. However, the underlying mechanism responsible for such an impact is still debatable. All previous studies focused on a geochemical process where fluid-fluid interaction has been overlooked. Recently, some studies have indicated that brine-crude oil (micro-dispersion) interactions play dominant roles in improved oil recovery in carbonate rocks. Nevertheless, at the moment, no commercial simulator can mimic this mechanism from the perspective of fluid interactions. In this work, we investigated whether micro-dispersion is applicable in commercial reservoir simulators through the history matching of two carbonate coreflood experiments. In this part of the investigation, three aspects will be addressed. (i) Develop a correlation of the link between the mechanism (micro-dispersion) in the lab and numerical simulation. (ii) Predict the low salinity relative permeability curves. (iii) History match the experimental data. This paper presents an integrated method of simulating low salinity water floods in carbonate rocks.
Two different approaches have been applied to the history matching of unsteady state coreflood experiments. First, numerical simulation was performed to extract the high salinity relative permeability curves (KrHS) of the secondary mode for both experiments. Then, the findings from the first approach and the experimental results were used to develop a new approach for predicting the low salinity relative permeability (KrLS) curves. The new approach was not only used to predict KrLs curves through micro-dispersion but also used as input to history match the tertiary low salinity water floods. An excellent match was obtained using both the numerical simulation model and the new approach for the oil recovery and pressure drop profile, where two different relative permeability sets were generated in this study for each coreflood. The first observation promotes the premise that a history match of a coreflood can be obtained using different sets of relative permeability curves. In contrast, the Corey exponents, residual oil saturations and endpoints are essential parameters in the history matching of LSWF. The results obtained in this study will help to understand the modelling process involved during oil recovery by LSWF and introduce a new approach to model the effect of LSWF.
Characterization of Native Colloids and Study of Emulsions Stabilized by Asphaltene, Wax, Silicates and Calcites Using Optical Analyzer Turbiscan
Authors: Abubakar Umar Abubakar , Mohd Saaid Ismail , Adebayo Sulaimon Aliyu , Mohd Pilus Rashidah , Amer Nurul Asna , Halilu Ahmed , Mamo Negash Berihun 
Published: March 2019

Abstract
Water-in-oil petroleum emulsions were prepared using response surface methodology (RSM) based on box-Behnken design (BBD). The emulsions were prepared using a treated Malaysian offshore crude oil, where the saturates, aromatics, resins and asphaltenes (SARA) of the crude oil were extracted using a modified SARA analysis. Other native solids, wax and asphaltenes extracted from oilfield emulsions and other crude oils were used as the emulsifying agents. In this paper, we focus on the characterization of some oilfield solids extracted from Malaysian offshore fields and further investigated their potentials to stabilize petroleum emulsions. The effects of the solids alone, and in combination with asphaltene/resin and wax were studied using statistical methods and the stabilities of the emulsions examined using a Turbiscan optical analyzer. The main advantage of Turbiscan is to obtain a faster and more accurate detection of destabilization phenomena in non-diluted emulsion than can be detected by the naked-eye (observation), especially for an opaque and concentrated dispersion system. The sample characterizations were conducted with FTIR, TGA, FESEM/EDX, XRF and XRD. Results showed that the major native solids present in the samples were paraffins and calcium carbonate. Further analysis revealed that the solids by themselves do not significantly contribute to emulsion stability. However, in the presence of asphaltene/resin compounds, the prominent solids such as paraffins and calcium carbonate enhance the stability of the emulsion irrespective of asphaltene/resin concentrations.
New Development Concept Lead to Five Folds Recovery Increase in a North Kuwait Field
Authors: Daoud Ahmed M. , Al-Otaibi Basel , Alkandari Dhuha 
Published: March 2019

Abstract
One of the North Kuwait Carbonate fields which starts its production in 1957 has very low recovery factor after 60 years of production although the field was under water flooding since 1997. A workflow was developed to first understand the reason behind the low recovery and second to propose the best way to improve it.
The workflow starts with first building a material balance model to understand the main reservoir driving mechanisms. Second, a fine-scale history matched simulation model was used to understand the main reasons of the current low recovery. A Produce High and Inject Low (PHIL) concept was proposed with locating all the injectors at the deepest zone and the producers at the shallow zones. Finally, the proposed PHIL concept with inverted 5-spot horizontal wells was examined compared to the inverted 9-spot vertical wells and to the peripheral PHIL concept using the simulation model to examine the best approach to maximize the recovery.
Different outcomes from the above-mentioned workflow can be summarized as follows; first, it was found that the main driving mechanism is water injection which represents 70% of the reservoir recovery factor. Hence the importance of creating an artificial aquifer along the whole area of the field to provide the required pressure support which calls for the implementation of the PHIL concept with inverted 5-spot pattern background as the best development concept for the field. Second, the thorough data review used on building the fine-scale model shows that the current recovery is dominated by single zone which represents only 15 % of the in-place and on top of this, it was found that all the developed wells are located only on 30% of the field leaving 70% of the field undeveloped. These are the main reasons behind the low recovery. Finally, the developed PHIL concept with inverted 5-spot background shows that the recovery can be increased by five times with less number of new wells and less water injection volume required compared to the 9-spot vertical wells and the peripheral PHIL concepts. This five-folds increase in recovery encourages the asset to do a pilot to implement the proposed development strategy.
Unlike the commonly used inverted 5-spot vertical wells, this work proposes a novel approach of inverted 5-spot horizontal wells with directing the horizontal injectors at the deepest zones and the horizontal producers at the shallow zones. Hence creating an artificial bottom aquifer with minimizing the water production and maximizing the water injection distribution along the whole area of the reservoir.
Channel Fracturing Technology to Successfully Deploy Proppant Fracturing Stimulation Under Limited BHP Window for Completion Integrity
Authors: Buenrostro Adrian , Harbi Abdulaziz , Arevalo Alfredo , Carmona Jairo 
Published: March 2019

Abstract
Study made from the results observed over a particular application objective with one of the recently developed proppant fracturing techniques known as Channel Fracturing. This technique was used in this application to place a proppant fracturing treatment in a tight gas reservoir which pushes the installed well completion to reach its mechanical limit capabilities. Channel (or pillar) fracturing was applied in multiple cases with the intention to constrain the pressure increase commonly observed during a fracture job execution.
Detecting Emulsion Using Surface Temperature, Pressure, and the Application of Artificial Intelligence
Authors: Esbai Rami , AlRumaidh Ahmed , Sharaf Sayed 
Published: March 2019

Abstract
Innovation in the analysis of oil well surface measurements has led to the discovery of an instantaneous and straightforward emulsion detection calculation. When applied in the Bahrain Field, this led to the treatment of emulsion in over 100 wells, resulting in a cumulative production gain of over 500,000 barrels to date at negligible cost. Artificial Intelligence (AI) was then employed to identify and understand factors related to emulsion and optimisation treatment programs. Once the wells were treated and the method was confirmed to prove emulsion existence, a focused approach was carried out to understand it further. Wells were categorised based on their production response to standard demulsifier bullheading.
In addition to a variety of well parameters, this data was used to build a machine learning model that helped identify patterns with regards to problematic zones, properties of wells with emulsion, and the best treatment method for each well. The results of the study were rather substantial and resulted in numerous new insights. Firstly, a model was built to predict the sustainability and economics of expected bullheading job treatments. This is currently being used to rank the priority of wells for either bullheading treatment or continuous chemical injection. Once the wells were classified into basic sub groups and sorted by zones, geographic analysis was carried out to identify wells with emulsion being formed as a result of waterflooding. This led to further insight into the nature of emulsion blocks, where in some cases, although it was found that these blocks exist downhole, traces of emulsion will flow to the surface and can have a unique signature.
This paper discusses in further detail insights into emulsion and the different types of AI algorithms used to answer questions raised as a result of the discovery. The necessity of using machine learning cannot be overstated enough and the observations made in the paper could not have been found if it were purely by observed by the naked eye.
The topic of emulsion is highly understudied, and the concept of using the emulsion detection calculation was not published before. In addition to highlighting this discovery, this paper can influence other operators to test their findings and have a real world application of machine learning in their fields.