bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of Machine Learning Approach on the Results of a 3D Grid Model to Identify Impacting Uncertainties and Derive Low/High Production Profiles, FRF Team
Authors: Van-Dunem Martins Bruno , Lesage Alexandre , Rondeleux Baptiste , Thomazo Nicolas , Barrault Philippe 
Published: October 2022

Abstract
There are two main methods to incorporate uncertainties in the production profiles. The first option relies on multiple geomodels and reservoir simulations that require long engineering time but help to assess accurately the low and high case profiles integrating the main geosciences and reservoir uncertainties. The second option is to use Decline Curve Analysis (DCA) with a reduced engineering time. Nevertheless, this method relies solely on production data, ignoring the geology and various production mechanisms. Process automation and machine learning workflows can then be of great help in this second case but remain generally limited, lacking transverse features for the geology and production mechanisms.
In this paper we will present a hybrid approach where the low and high case production profiles were stochastically generated, derived from a deterministic basecase model simulation.
This approach was tested on a giant carbonate field in the Middle East with a few million grid cells and hundreds of wells. The study was provided with a basecase history matched model and its associated forecast run.
The interest of the method is to save time as the hybrid approach gives faster results compared to existing methodology while including geological and dynamic features to generate low and high cases for production profiles
Machine learning based workflows were developed to calculate and extract from 3D timelapse models and 2D results all the important parameters (well features) driving well performance in the history but also in the forecast.
Those important features were used to train a machine learning random forest model to predict the cumulative production (Np) of the wells. In addition to the ability to predict the Np, the model also gives the impact of every parameter on the cumulative production, allowing a ranking of the most impacting production mechanisms and geological parameters.
The distance to the waterfront at the time the well was drilled was identified as the major parameter impacting the cumulative production; this feature and other key parameters were therefore used to generate the low and high cases.
The final outputs from the study were the delivery of the low and high production profiles, taking into consideration the main production mechanisms and geological uncertainties identified and turned into features for the workflow.
This work was done thanks to a multidisciplinary team composed of Reservoir engineers and datascientists from the FRF team.
Machine-Learning-Based Automation of Formation Testing Data Interpretation and Analysis
Authors: Ramatullayev Samat , Mustapha Hussein 
Published: October 2022

Abstract
Formation testers have become an essential component of every well data acquisition campaign for exploration and appraisal. They are used for reservoir pressure and mobility profiling, as well as downhole fluid analysis and sampling, microfracturing, and other applications.
Hundreds of formation testing jobs and thousands of pretests are performed on a daily basis around the globe, whether they be a on wireline or while drilling. Each pretest is handpicked by an engineer to classify the test type and select the representative reservoir pressure and mobility. The operator collects all processed data and analyses them in an integrated fashion—at the well and multiwell levels—to obtain insights. The whole process is still very manual, and it could take up to 2 weeks in the best-case scenario.
The aim of this paper is to introduce artificial-intelligence (AI) components to the data interpretation and analysis workflow for formation testing data. We aim to leverage human-in-the-loop automation to an entire workflow using machine-learning (ML) algorithms, reducing turnaround time from weeks to minutes while also generating new insights.
Using a combination of traditional and ML algorithms, automation will affect: raw-data ingestion process; pretest interpretation (extraction of reservoir pressure, mobility, and other key features along with classification of pretests into different types), well-level analysis (automated detection of reservoir zones using well log data for gradient selection along with optimal pressure gradient selection); and multiwell-level analysis and generation of ready-to-go data for reservoir characterization and simulation.
The AI solution includes many supervised and unsupervised ML models, as well as traditional algorithms for calculating and extracting key features. The AI workflow was tested on a variety of previously unseen data from various wells drilled in clastic and complex carbonate reservoirs with varying reservoir pressure and mobility. The results showed that the ML model was very accurate in predicting pretest types, distinguishing reservoir zones using well logs, and selecting the best fluid gradients automatically.
The solution was built in an enterprise-scale AI/ML data science studio that allows us to deploy it across the entire organization.
The novelty of the solution is an automation of the entire workflow using a mixture of AI and traditional algorithms from pretest to multiwell analysis and an integration of the results into static and dynamic models for further history-matching workflows. The use of this product in real-world data would result in an order-of-magnitude reduction in the time engineers spend on this task and lightning-fast insights.
Compositional Uncertainties in Laboratory PVT Data
Authors: Bilal Younus , Curtis Hays Whitson , Sissel Martinsen 
Published: October 2022

Abstract
Accuracy of phase behavior and volumetric calculations from a cubic equation of state (EOS) depends on the accuracy of the molar compositions used as input to the model. Lab-reported compositions have uncertainty, like all other measured PVT data. This paper discusses different sources of uncertainty in lab-reported compositions, the magnitude of uncertainty, and we propose methods to correct for uncertainty that improve PVT calculations of individual samples.
Lab-reported molar compositions can have uncertainty due to (a) baseline shift and (b) internal standard used in gas chromatography, (c) component molecular weights used to convert measured mass fractions to mole fractions, and (d) the gas-oil molar ratio (i.e., gas-oil ratio) used in recombination. A molar distribution model is used to assess and quantify uncertainty in chromatographic measurements of heptanes and heavier (C7+) fractions, also providing a method to correct for possible errors.
As a theoretical basis, synthetic examples are used to demonstrate the application of the gamma molar distribution model to quantify and correct compositional uncertainty in C7+ mass fractions due to baseline shift and internal standard. The workflow includes use of a distribution model that describes more than 50 PVT samples with widely varying gas-oil ratios and API densities, all from the same basin / field, and analyzed by several PVT laboratories over an entire decade.
Examples show that a common distribution model reliably corrects for compositional uncertainty from baseline shift and internal standard errors. The model also provides consistent and representative estimates of C7+ component molecular weights that are used to convert masses to moles. The same model provides consistent sample-specific average C7+ molecular weights that are used in correlating property variations across the basin.
Most engineers use the lab-reported molar composition "as is" from a PVT report, often directly as input to an EOS model. We show quantitatively the four reasons why a lab composition may have systematic error. We also provide methods to quality check and correct lab-reported compositions. A molar distribution model is used to model heavier (C7+) components quantified by gas chromatography, where the model can be used to identify errors introduced by internal standard and baseline shift issues. The proposed methods are illustrated for an entire basin where more than 50 samples have been used, covering a wide range of GOR and API.
To our knowledge, this is the first attempt to identify and deal with composition errors with a systematic and comprehensive workflow.
Modeling a Successful Pilot Application of Novel Fine Stabilizer Technology to Predict Oil Well Productivity: A Benchmark for Field Replication
Authors: Hendraningrat Luky , Ramly Nora Aida , Riyanto Latief , Baharuddin Salina , MousaviMirkalaei Seyed Mousa 
Published: October 2022

Abstract
A novel developed fines stabilizer (FS) chemical was used to successfully treat an oil well by reducing the decline rate from 17% per month (pre-treatment) to 1.5%/month (post-treatment for a 6-month monitoring period). Following this success story, more qualified candidates are flocking to replicate it. As a result, the challenge now is to predict each candidate's post-treatment performance. The paper focuses on the development of numerical modelling of a fine stabilizer chemical using a chemical reaction approach to understand the mechanistic process and will serve as a benchmark for future technology replication performance prediction.
The mechanisms of the novel developed FS chemical were observed in the laboratory through fine particle coagulation and flocculation. The methodology of this study is to translate the mechanisms and key features associated with observed laboratory data into a scripted chemical reaction program that was coupled with a numerical reservoir simulator. Prior to well modelling, laboratory coreflooding data were validated. To properly represent the mechanistic process with a chemical reaction approach and capture the near wellbore effect, a single well model with local grid refinement was developed. The chemical reaction feature provides a versatile toolbox for modeling complex processes involving chemical and physical interactions. The actual production data history matching process with was carried out to investigate key important parameters.
The FS chemical reaction was divided into two stages: damage and treatment. The damage was defined as fines deposited in the pore throat plugging and reducing permeability near the wellbore. Fines migration frequently indicates a build-up of fines in the near-wellbore region over time. As a result, the damage caused by these deposited fines reduces permeability. The novel FS chemical will remove deposited fines through micro-flocs and coagulating fines as solid. The history matching process was completed for core data and the mechanistic model with production and pressure data, and acceptable matches were obtained using rate control. The mechanistic model was tested with constrain at bottomhole pressure for few months historical data. It can predict the production performance with good accuracy compared to actual welltest data.
Key parameters of FS injection were observed because of this research and can be used as a benchmark in the future to demonstrate the concept of extending oil well productivity and predicting field replication to recurrence the success story.
Short and Long-Term Fracture Permeability Tests Under Stress on Pre-Salt Low Permeability Carbonates After Acid Treatment or with Proppants
Authors: Su Kun , Barlet Pierre , Borgomano Jan V.M. , Castets Melissa , Caillet Cedric , Okullo Ruhhiyyih , Azevedo Cecilia , Ferreira Francisco , Beele Magali , Coelho Dimas , Costa Francileide , Sá Andrea , Schwalbert Mateus , Abreu Carlos , Silvany Pedro 
Published: October 2022

Abstract
For relatively low permeability Pre-salt carbonates reservoirs, the question of progressive closure of the stimulated and acidized fractures is raised since their evolution over time could be the main cause of the production decline. Aiming at quantifying the time-dependent behavior and stress sensitivity of the acidized fracture or propped fracture, a series of fracture permeability tests were carried out in laboratory on tight Pre-salt carbonate samples from various facies of a Brazil offshore well. For creating fracture, direct shear tests were performed on initially intact cylindrical carbonate plugs in a conventional triaxial cell. The fresh fractures are then submitted to acid flow over certain duration before permeability measurement under stress. Long term fracture permeability tests lasting over several months have been carried out and were compared with the short-term tests. Correlation with mechanical properties were also investigated. We compare the performances of propped fracture to acid washed one to formalize recommendations for fracture stimulations in the Pre-salt carbonates. The propped fractures clearly performed well, with negligeable stress-sensitivity on fracture conductivity and no noticeable effect of time over 50 days. Mixed results were found for the acid-washed fractures, in which some showed little stress-sensitivity and little time-effect while others the contrary.
Data-Driven Prediction Method of Water Cut Based on Random Forest Regression Model
Authors: Chen Yihang , Zhu Yimeng , Li Yong , Hu Dandan , Shi Shuzhe , Chen Yajing , Li Qianyao , Gu Fei 
Published: October 2022

Abstract
Water cut (WCT) is a key parameter to analyse the performance of wells and reservoirs within a producing oilfield. However, the WCT data recorded in the life term of a well may not always be accurate or available, which may lead to the potential problem with well and reservoir models constructed with the data. This can lead to errors in predicted future well and field production, or missed opportunities for well workover activities. This paper describes a case study where the WCT of producing oil wells from a large Middle Eastern oil reservoir was modeled using random forest regression in order to identify errors and improvements in the field data. Pressure data and fluid properties were input as training variables and the model was evaluated by cross-validation. The relative importance of these variables was calculated and the coefficient of determination (R2) between the observed and predicted WCT of the test set was used to evaluate the model performance. It was found that the apparent density of the producing fluid and the variables related to the fluid composition have strong connections with WCT, as would be expected based on traditional vertical pipe flow theory. For the wells with good field WCT data the model accurately matched the real field data. For the wells with poor or absent field WCT data the model was used to predict the WCT and significantly enhance the dataset with a high degree of confidence. It is concluded that the random forest regression model can predict the WCT based on other well surveillance data. Overall, the current study provides an approach to integrate multiple factors of surveillance data to calibrate the WCT data, and can add significant value to well and reservoir models for the purpose of accurate production dynamics analysis and forecasting.
Development of a Machine Learning Technique in Conjunction with Reservoir Complexity Index to Predict Recovery Factor Using Data from 18,000 Reservoirs
Authors: Pooladi-Darvish Mehran , Tabatabaie S. Hamed , Rodriguez Cadena Camilo 
Published: October 2022

Abstract
With increased popularity and success of Machine-Learning (ML) Techniques, there is continued interest in developing new tools while exploring the boundaries of the technique. Here we use ML techniques to identify reservoir engineering, geological and development features that influence the ultimate recovery factor in a group of reservoirs, and to develop a model that provides the relation between recovery factor (RF) and these influencing features. Furthermore, we use techniques that allow opening the "black-box model" to decipher the relation between the model output and the individual features.
The overall process included: (i) Creating a subset of reservoirs from the main database that included high quality reliable data, (ii) using SME (Subject Matter Expert) knowledge to develop a list of expected influential attributes, (iii) investigating the quality and the quantity of the data within each attribute and imputing when necessary, (iv) utilizing data analysis tools to reduce the number of input variables, and (v) examining the success of the model in modelling the test data. A Reservoir Complexity Index (RCI) factor was calculated using the 6 attributes identified and input into the ML model.
For a dataset of approximately 18,000 reservoirs with high confidence in the recovery factor, a model was developed with 6 numerical features. The model exhibited an accuracy of ±5% for 50% of the data (between P25 and P75) and ±10% for 80% of the data (between P10 and P90). Contrary to our expectations, inclusion of the categorical features available in the reservoirs’ database did not improve the accuracy of the model.
While this work falls within a growing category of similar work, its novelty is in demonstrating the strength of XGBoost in modelling and of SHapley Additive exPlanations (SHAP) in deciphering the relative contributions for each attribute of the model. Furthermore, we provided multiple examples to demonstrate and explore both the value and the limitations, and failure of application, of the ML techniques in this kind of work. This learning is important for a cautious but robust adaptation and acceptance of ML techniques.
SCAL for a Giant Carbonate Reservoir: A Critical Step Towards Identifying EOR Targets
Authors: AlSumaiti Ali , Masalmeh Shehadeh , Shaik Abdul Ravoof , AlShehhi Budoor 
Published: October 2022

Abstract
Relative permeability, residual oil saturation and capillary pressure curves are key inputs to understand and predict reservoir behavior in all phases of development and define the target for enhanced oil recovery (EOR). Experimental techniques to measure residual oil saturation (Sor), relative permeability (Kr) and capillary pressure (Pc) curves are well established. However, we frequently encounter quality compromised data due to challenges in experimental procedures, insufficient understanding of experimental techniques, and poor quality of raw data. As a result, this could lead to compromised development plan and poorly defined target for EOR.
The objective of this paper is to present a comprehensive laboratory assessment using different experimental techniques, quality assessment/control (QA/QC) of raw experimental data and numerical interpretation of qualified laboratory tests to evaluate and obtain representative residual oil saturation and relative permeability curves for a Giant carbonate reservoir. The work also compares data obtained using composite and single core samples and highlights the challenges to obtain quality SCAL data using composite core steady state experiments.
Conventional and special core analysis program is conducted using different rock types with variable petrophysical properties. The SCAL program includes steady state coreflood with X-Ray monitoring, porous plate experiments (PCRI) and ultra-fast centrifuge both single and multi-speed to measure residual oil saturation, relative permeability (Kr) and capillary pressure (Pc) curves. Steady-State coreflooding and PCRI experiments are performed at reservoir conditions, while centrifuge runs are done at 80°C. The Steady state experiments are executed on both single plug and composite core samples. Numerical simulation is used to reconcile the various SCAL datasets and derive a consistent set of saturation functions.
The main conclusions of the study are: 1) Qualified advanced core analysis experiments have successfully revealed comparable Sor and Kr curves from the different techniques, 2) Initial water saturation is independent of core permeability and found less than 15%, 3) Residual oil saturation is found independent of the sample’s permeability and consistently less than or equal to 15%, 4) Most of composite coreflood tests are disqualified as the data is strongly dominated by capillary end-effect and core heterogeneity, while single plug floods have better quantification of Sor and Kr curves
Unlocking Oil Potential in Mature Oil Fields Using Sand Agglomeration Technology and Eliminating Artificial Lift Pre-Mature Failures
Authors: Mandhari Sulaiman , Al-Amri Nawal , Riyami Said , Busaidi Iman , Saadi Faisal , Kayumov Rifat , Hilal Ahmed , Trejo Ricardo 
Published: October 2022

Abstract
An advanced sand agglomeration technology (SAT) is proposed as a solution for the sand production issues that commonly occur in many oil-producing sandstone reservoirs in the Sultanate of Oman. In the past, several techniques have been applied to improve well productivity and increase the run life of artificial lift prior to failure due to sand production. Mechanical solutions, such as standalone screens or gravel packs, are difficult to handle and to recover after they fail. In addition, the aim of existing technologies is to minimize the sand issues inside the casing and not to stop sand production within the formation. Resins can be effective against sand production, but resin treatments noticeably reduce well productivity due to the negative effect on reservoir permeability in the critical near-wellbore zone.
An advanced SAT is an effective solution against the sand production problem. The SAT is pumped into the formation to tackle the sand issue and convert weakly consolidated, semiconsolidated, or unconsolidated formations into agglomerated formations. The SAT alters the zeta-potential of the unconsolidated sand grains to the optimum value. This causes agglomeration of the grains and eliminates or significantly reduces sand production.
Integrated technical work resulted in the successful implementation of SAT treatments in Oman. The treatments consisted of a simple bullhead operation with minimum equipment and personnel on the wellsite. After the treatments, the performance of the wells was closely monitored for 2 years. The performed SAT treatments saved the cost of frequent backflushing and hoist interventions by increasing the run life of artificial lift without affecting the well production. Prior to the SAT treatments, backflushing and hoist interventions were commonly required in these two wells. These interventions were not needed in these wells after SAT treatment.
The advanced SAT was piloted for the first time in Oman and showed very promising results. The experience and knowledge gained during this trial campaign will be used during planned full-scale deployment of SAT treatments in other unconsolidated sandstone formations in Sultanate of Oman.