bluuebunny/crossref_metadata_2025 huggingface dataset:
Generation of a Regional Fluid Database for Gas Condensate Assets in a Thrusted Carbonate Environment of the Northern Emirates
Authors: Saradva Harshil , Golaco Christna , Robert Matthew , Jain Siddharth , Callum Thurley , Bentaher Abdulhafid , Al Hamadi Masoud
Published: October 2022

Sharjah National Oil Corporation (SNOC) operates 4 onshore fields, the largest of which has been in production since the 1980's, in addition to over 50 years of exploration activity in the region. The producing fields exhibit a wide range of condensate-gas ratio (CGR) productivity and other properties. The scope of this paper is to discuss how the data from these exploration and development wells was combined to develop a trend of the fluid properties within the Northern Emirates to assist in the Future SNOC activities.

A detailed data scouting and processing workflow was undertaken and all available legacy pressure-volume-temperature (PVT) reports were digitized. All available separator recombined samples reports and bottomhole sample reports were compiled into a master database. The well test reports were thoroughly examined to understand the field operations and determine the reliability of the PVT reports while the fluid samples were collected during the well tests. An equation of state was generated for each field using all available information taking into account their production history over 30+ years and the results were then used for the regional PVT study.

A distinct trend of CGRs, specific gravity and other reservoir fluid properties were observed which co-related with the formations. These prospects which are spread out all over the Emirate of Sharjah were compared on the same parameters to develop a regional guideline. A regional trend of changing fluid properties was also observed which helps define the fluid properties expected in the Thamama Group formations in the Northern Emirates from any exploration well. The results will assist in determining the valuation of any exploration prospect if it is deemed successful and also plan ahead for the value of the prize. These trends also helped fill in missing data and perform quality control (QC) on older fields where comprehensive lab data was unavailable. In some cases, gas condensate fluid properties were unattainable at the mature stage of the field and this study provided the necessary information to plan enhanced recovery opportunities.

This paper aims to be a leading reference for the PVT fluid properties in the complex Thamama Group of the Northern Emirates to support future exploration and development activities. The regional data provides a strong correlation and this information is novel in terms of utilizing legacy data for potential opportunities. The new database also helps QC the dataset of existing PVT reports along with aiding in identifying the sources of hydrocarbon origin in this region.
Evolution of Well Completion Strategy in Deep High-Pressure Sour Environment to Enhance Rig-Less Operations & Optimize Rig Workover Operations
Authors: Al-Hajri Hamood , Al-Subhi Anees , Khayrutdinov Fanis , Al-Busaidi Asaad , Al-Shukaili Ibrahim , Al-Abri Humaid , Al-Abri Zahir , Al-Hadhrami Abdullah
Published: October 2022

Deep critically sour fields played a key role in PDO's production portfolio for past two decades particularly in the south of Oman. Cost-effective solutions to develop these fields was certainly needed to ensure that oil produced from these fields is economically attractive. Completion design optimization was successfully implemented leading to significant savings in life-time cost of these well.

Completion optimization journey was performed in stages based on the challenges encountered throughout the life of the field. Due to risky nature of the reservoirs in these fields, wells were completed with tubing and permanent packers to ensure well integrity requirements are fulfilled and all operation risks are controlled. These permanent packers induced a big challenge during well intervention & workover as they have low success chances when it comes to milling. This base completion design was optimized including eliminating the use of permanent packers to achieve savings of about 5% in well cost at least 80% in well interventions.

At early stage of the field life, combining drilling caprock and reservoir section and casing them with the same liner was considered as a good saving in well drilling cost leading to total cost reduction of about 5% of total cost. However, this optimization increased cost of well intervention dramatically in these wells as the tubing and packer need to be retrieved to gain full access. In addition, challenges in milling the permanent packer have increased intervention cost even further. This first optimization done was to introduce a liner across the reservoir section of the same size as the tubing (Mono-bore completion). This optimization made the completion more Rig-less friendly and reduced well intervention cost by at least 80%. The second optimization done was to replace the permanent packers with Tie-back Polished Bore Receptacle (PBR). This has eliminated the requirement for packer milling without jeopardizing the sealing functionality of the permanent packer. This design was even optimized further to eliminate the possibility of failure of the permanent packer or PBR by introducing cemented completion. Cemented completion has also reduced total cost of drilling & completing the wells by 5% while maintaining the advantage to perform well activities and minimum cost.

These improvements in well completion strategy have reduced life-time cost of the well dramatically without jeopardizing well basic functional requirements and well integrity requirement. As a result of this work, production from these field became more economically attractive.
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

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

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

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

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

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

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

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

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

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.
Unique Synergistic Surfactant Mixture to Match Ultralow Interfacial Tension with Solubility at High Temperature and High Salinity
Authors: Herman George , Lichterfeld-Weber Nicole , Bittner Christian
Published: October 2022

Chemical enhanced oil recovery offers the huge benefit to reduce the carbon footprint in oil production. Mature oil fields with existing infrastructure can be turned into fields with higher productivity. Thereby, costly exploration of new fields can be avoided. The rejuvenation of mature oil fields can be achieved by surfactant flooding. Well-designed surfactants can reduce the tension between crude oil and water to ultralow values and thereby mobilize physically trapped oil. With respect to fields of high salinity and elevated temperature, it is particularly challenging to match the reduction of interfacial tension with the surfactant solubility as well as the chemical stability. In this paper, the solubility of surfactants in model reservoir brine (rich in calcium and magnesium ions) at elevated temperature was evaluated. The capability of surfactants to reduce interfacial tension between light crude oil and model reservoir brine was tested by phase behavior tests as well as by spinning-drop experiments. The thermal stability of stored surfactant solutions was analyzed by use of high-performance liquid chromatography (HPLC). The focus of the investigations was a temperature above 100°C and salinities of at least 100,000 ppm TDS for fields with light crude oils. Alkyl ether carboxylates are chemically very stable and offer a good potential to reduce interfacial tension. However, they usually lack sufficient solubility. In contrast, cationic surfactants are well soluble under such harsh conditions. However, they typically lack chemical stability and sufficient potential to reduce the interfacial tension. By taking the typical degradation pathways of cationic surfactants into consideration, it was possible to identify a cationic surfactant that is stable under harsh conditions. A well-designed formulation of the alkyl ether carboxylate with the cationic surfactant allows to match reduction of interfacial tension with solubility. Several other surfactant classes and their combinations were evaluated, but they were not able provide similar performance. This study reveals a unique synergistic surfactant mixture, which provides ultralow interfacial tensions against light crude oil at elevated temperature of 125°C and at high salinity 139,000 ppm TDS. In addition, the surfactant mixture is soluble and chemically stable under such harsh conditions.
A Success Story of the First Miscible CO2 WAG Miscible WAG Pilots in a Giant Carbonate Reservoir in Abu Dhabi
Authors: Mabkhout AlSeiari Reem Ali , Bhushan Yatindra , Igogo Arit , Singh Manjit Kumar , Marzooqi Saeeda Al , Al Hammadi Shareefa Ahmed , Khan Sara Hasrat , Mohamed Moza Abdelrahman , Brodie James , Tenaiji Aamna Al , Al Ali Mona Yousif , Al Keebali Aaesha Khalfan , Masalmeh Shehadeh
Published: October 2022

The key objective of the CO2 WAG Pilots is to confirm improved sweep and to enhance oil recovery under CO2 WAG relative to water flooding. Two CO2 WAG Pilots are in progress in a giant Abu Dhabi Oil Reservoir. Each pilot has one horizontal producer and two horizontal injectors along with two vertical pilot observers. A detailed monitoring plan was designed and implemented to monitor pilots’ performance and track CO2 breakthrough and flow path. Injectivity of both water and CO2 was determined in the WAG cycles to investigate any loss of injectivity.

The producers are being tested daily for oil rate, water cut, GOR using multi-phase flow parameters (MPFM) while portable test separators are used every quarter to validate these measurements. PVT analysis of produced fluids are being carried out on samples from portable test separators and MPFM sampling point to monitor CO2 content.

Different gas and water tracers have been injected to trace the movement and breakthrough of injected fluids into the pilot producers. Carbon and oxygen-isotope analysis for produced and injected CO2 gas is also carried out to monitor CO2 breakthrough.

RST logs in the observers demonstrate good sweep across different layers of the reservoir and show that WAG is providing mobility control to CO2. Several data sources were analyzed to determine CO2 breakthrough time and the CO2 flow path. Analysis of CO2 in produced gas has determined the timing of CO2 breakthrough. This is supported by the isotopic analysis of injected and produced CO2 in pilot producers and near-by producers. The tracer analysis results unambiguously identify the source of the produced CO2. Injectivity analysis of both CO2 and water showed injectivity of CO2 was either the same or higher than water injectivity. Moreover, no loss of injectivity was observed between WAG cycles. The pilot has been operated successfully without HSE issues since 2016. Corrosion logs are acquired within the extensive monitoring program along with inhibitor injection to avoid any Asphalting deposition.

The paper discusses the performance of the first multi-well CO2-WAG pilots in a giant onshore reservoir in Abu Dhabi which is used to de-risk multiple CO2 WAG full field projects in ADNOC reservoirs. It also highlights the importance of the different reservoir monitoring tools for improved understanding of the pilots which will be used as a basis for implementing CO2 WAG for the full area development.
CO2-GOGD Subsurface Analysis in Fractured Carbonate in the Sultanate of Oman
Authors: Bos Robert , Mahruqi Dawood
Published: October 2022

The North Oman "A" Field is a giant fractured carbonate reservoir, that has been in production for more than 50 years. In recent decades, the predominant production scheme has been via Gas Oil Gravity Drainage (GOGD). In this scheme, hydrocarbon gas is injected into the high permeability fractures to allow for oil production from the low permeability matrix. In this paper we will study the option of replacing the hydrocarbon injection gas by sub-critical CO2 gas. This would have the advantage of storing CO2 in the matrix while making more hydrocarbon gas available for sales.

For the CO2 injection to be successful, the CO2 that is injected into the high permeability fractures needs to move into the low permeability matrix rock, even though there is no forcing viscous drive mechanism.

The mechanisms by which the CO2 can go into the matrix are:

Gas-Oil gravity drainage (CO2 gas in fractures is pulled into matrix to replace produced oil) Gas-Gas gravity drainage (CO2 gas in fracture has higher density than hydrocarbon gas in the matrix) Diffusion (Differences in concentration differences lead to CO2 moving into matrix).

In this paper we will present both analytical models and numerical modeling that show the relative contribution of each of these mechanisms.

In term of relative contribution, both analytical and numerical modeling shows that Gas-Gas gravity drainage is the most effective method from transferring denser CO2 gas to the matrix, followed by gas-oil gravity drainage. Diffusion only provides a small contribution.

In absolute terms, modeling results indicate that significant volumes of CO2 and commercially significant volumes of hydrocarbon gas can be recovered from the reservoir.

Overall, our assessment is that from a subsurface perspective, switching injection gas in GOGD from hydrocarbon gas to CO2 is attractive from both a commercial and an environmental perspective.

The novel contribution in this paper is that we present a subsurface analysis of a switch from hydrocarbon gas to CO2 in GOGD developments. This analysis is relevant because it provides a way to store CO2 while also providing extra sales gas.
Foam-Oil Displacements in Porous Media: Insights from Three-Phase Fractional-Flow Theory
Authors: Tang Jinyu , Castaneda Pablo , Marchesin Dan , Rossen William R.
Published: October 2022

Foam is remarkably effective in the mobility control of gas injection for enhanced oil recovery (EOR) processes and CO2 sequestration. Our goal is to better understand immiscible three-phase foam displacement with oil in porous media. In particular, we investigate (i) the displacement as a function of initial (I) and injection (J) conditions and (ii) the effect of improved foam tolerance to oil on the displacement and propagation of foam and oil banks.

We apply three-phase fractional-flow theory combined with the wave-curve method (WCM) to find the analytical solutions for foam-oil displacements. An n-dimensional Riemann problem solver is used to solve analytically for the composition path for any combination of J and I on the ternary phase diagram and for velocities of the saturations along the path. We then translate the saturations and associated velocities along a displacement path to saturation distributions as a function of time and space.

Physical insights are derived from the analytical solutions on two key aspects: the dependence of the displacement on combinations of J and I and the effects of improved oil-tolerance of the surfactant formulation on composition paths, foam-bank propagation and oil displacement. The foam-oil displacement paths are determined for four scenarios, with representative combinations of J and I that each sustains or kills foam. Only an injection condition J that provides stable foam in the presence of oil yields a desirable displacement path, featuring low-mobility fluids upstream displacing high-mobility fluids downstream. Enhancing foam tolerance to oil, e.g. by improving surfactant formulations, accelerates foam-bank propagation and oil production, and also increases oil recovery. Also, we find a contradiction between analytical and numerical solutions. In analytical solutions, oil saturation (So) in the oil bank is never greater than the upper-limiting oil saturation for stable foam (fmoil in our model). Nevertheless, in numerical simulations, So may exceed the oil saturation that kills foam in the oil bank ahead of the foam region, reflecting a numerical artifact. This contradiction between the two may arise from the calculation of pressure and pressure gradient using neighboring grid blocks in a numerical simulation.

The analytical solutions we present can be a valuable reference for laboratory investigation and field design of foam for gas mobility control in the presence of oil. More significantly, the analytical solutions, which are free of numerical artifacts, can be used as a benchmark to calibrate numerical simulators for simulating foam EOR and CO2 storage processes.
Embedding Physics and Data Driven Models for Smart Production Optimization. Field Examples
Authors: Mata Carlos , Badmaev Dorzhi , Saputelli Luigi , Mohan Richard , Rubio Erismar , Al Shehhi Maha , Salimov Rail , Eldjabri Mamar
Published: October 2022

The process of short-term water and gas flood optimization attempts to increase short term profit, while maximizing long term net present value (NPV) of the field. The characteristics of each production system would dictate how this process is achieved. Fields where the available producer well potential is significantly larger than the production quota could have infinite possible scenarios of production and injection well settings that would satisfy the field and reservoir production targets. But which of these scenarios maximize the long term NPV?

This paper explains the framework being implemented in ADNOC to streamline the optimization workflow, which runs both physics and data driven models, honours all constraints, and covers the associated processes from model maintenance, to calculation, execution, and monitoring. The workflows are orchestrated with a series of in-house interconnected digital solutions. This framework has been implemented in 5 production systems undergoing pattern injection of water, gas, and CO2. The associated digital solutions are well adopted by the asset teams.

Ability to optimize production and injection together has allowed the asset to focus on increasing injection capacity as the pattern, sector, and reservoir voidage constraints were identified to be the main constraint to production deliverability. The optimization scenario management and associated workflows have shown to deliver a gain of 1-3% of production by synchronizing the reservoir management process with the production operations business rhythm. The solutions have delivered so far more than 150 MM$ in value.
Test Protocol for Corrosion Resistant Alloys and Application to Material Selection for Ultra Sour Reservoir Containing Elemental Sulphur, in Continuation of SPE-203337-MS
Authors: Andres Bogdan , Vengan Chandrasekar , Mizukami Akio
Published: October 2022

Building upon the work detailed inSPE-203337-MS, the present work reports test results for the G3 alloy beyond the 4D limits defined by ISO 15156-3:2015/ NACE MR0175.

Corrosion of Oil Country Tubular Goods (OCTG) is a traditional concern in the Oil and Gas industry, as it represents a major component of well integrity management. Various corrosion mechanisms may affect the performance of materials in downhole applications, especially in corrosive environments containing large amounts of H2S, CO2, and Chlorides. In high sour environments, elemental Sulphur may also be present, in which case, it will act as a strong oxidizer, affecting the performance of the material selected.

In this paper, a test methodology was developed to select a techno economical fit for purpose material for application in the development of a Ultra Sour Reservoir Containing Elemental Sulphur. This methodology consisted in evaluation of the SCC (Stress Corrosion Cracking) behavior of Corrosion Resistant Alloys (CRA) of group 4D, as per ISO15156-3/ NACE MR0175-3, at the border between 4D and 4E environmental limits including the simultaneous presence of dissolved and elemental Sulphur in the fluid.

A testing set-up was developed to ensure the contact of elemental S with the material to ensure the strong oxidizing reaction at the surface of the material.

The behavior of the materials was assessed by Slow Strain rate testing (SSRT), C-Ring Sulphide stress cracking and Crevice corrosion test, conducted on UNS N06985.

CRAs have a greater CAPEX due to the content of costly chemical elements: Chromium, Nickel, Molybdenum, etc. However, they may be cost effective in a life cycle cost analysis by preventing workovers, assuring a higher HSE and well integrity level.
Solution for a Challenging Narrow Margin Deepwater Drilling Conditions - A Case Study from Lebanon
Authors: Martinez Jose Antonio , Kamel Mina Tawfik , Galiakbarov Dmitry , Bruce Stephen John
Published: October 2022

The requirements for offshore drilling continue to evolve to meet the needs of the industry, new technologies and drilling practices need to be adopted to accommodate these increasingly challenging environments. These new environments present challenges that include, but are not limited to, hole cleaning, wellbore stability, narrow margin drilling, and drilling fluid system stability over a wide range of temperatures. A high degree of planning and preparation is required in order to execute these wells, which will reduce any chance for a nonproductive time and deliver expected return on investment of the project. The need for a drilling fluid system that is designed to drill these challenging wells, is crucial to ensure the success of a project.

The well presented in this paper was the first well drilled in Lebanon and emphasizes the benefits of a low equivalent circulating density (ECD) impact drilling fluid when utilized in a deep-water environment. During the drilling phase of the project, the fluid was able to meet the demands of the operation by providing a low ECD while dealing with a range of temperature gradients that are associated with deep-water environments. The drilling fluid's unique performance ensured that any relevant nonproductive time events were mitigated and ensured the final delivery of the well. In addition, the fluid was able to meet these challenges with no additional treatment or modifications to the original planned fluid parameters for the project.

The low-impact, non-aqueous drilling fluid (LDIF) provided shear-thinning properties, flat rheological profile and non-progressive rapid-set/easy break gels. The fluid system benefited from a unique design that allowed the use of high yielding organophilic clay while utilizing an industry established low viscosity base oil. The synergy of the drilling fluid system when tailored to the base oil with kinematic viscosity as low as 1.7 cSt was able to provide rheological properties commonly associated to a clay-free non-aqueous base drilling fluid. The drilling fluid was able to provide a rheology that delivered a flat profile over the operating temperature range, while facilitating control of the ECD through the well. This operational experience shows how the choice of a proper fluid system together with a proper base fluid can ensure the unique challenges for the industry today can be successfully met.
Embedded Force Sensing at the Cutter
Authors: Koulidis Alexis , Zhan Guodong , Ahmed Shehab
Published: October 2022

Cutting and friction forces have been widely studied for PDC bits. Rock/bit interaction models are currently verified with a single cutter experiment and the utilization of multi-axis load cells. This paper aims to demonstrate an innovative method that experimentally validates the embedded cutter sensing on a laboratory scaled-drill bit to measure the forces acting on a single cutter.

A unique two-cutter drill bit design allows the integration of a sensor measuring the applied force on a cutter while drilling. A fully instrumented mini-rig is a key criterion to recreate the drilling environment. Control of the system and data analysis are performed while drilling, allowing continuous assessment of the drilled formations. Experiments are performed with new and worn cutters to independently evaluate the cutting and frictional components of the total measured force and compare them to the literature models. The high-frequency measurements at the cutter downscale the understanding that industry has on the bit-rock interaction and bring it to the cutter scale.

The effective cutting area changes dynamically depending on the position of the cutters in three dimensions and axial speed. The architecture of the mini-rig control system allows accurate control of depth of cut and interactive graphs of the acquired data. The cutter sensing enables spatial monitoring distribution of the cutting forces over the face of the scaled-drill bit.

Cutter/rock interaction models are validated with the acquired data from the cutter force sensing, enabling the evaluation of drilling efficiency while drilling different layers of rocks. Each cutter contributes to the weight-on-bit and torque-on-bit; thus, force sensing on a single cutter allows comprehensive performance evaluation for known drill bit design. The study of new and worn cutters provides insightful information on the drilling process in the cutter scale. In perspective, cutter force sensing might enable monitoring the wear evolution of individual cutters, which is impossible to detect with existing at-the-bit sensors.

The present work assesses the potential of integrating a force sensor at the cutter. The development of this work built the foundation for up-scaling research on cutter force monitoring.