bluuebunny/crossref_metadata_2025 huggingface dataset:
Changing the Game in Mature Field Development by Means of Advance Pulsed Neutron Logging as a One-Stop-Shop Solution for Formation Evaluation Behind Casing
Authors: Tanjung Heri , Cavalleri Chiara , Sianturi Julfree , Suwito Edy , Yasa I Made Agus , Rushatmanto Moch , Karana Anang , Suardiputra Aditya , Hardita Dimas , Wijaya Geraldie Lukman , Manalu Dasa , Sinaga Humisar Alprialdus , Lodiman William , Dewanda Ratna , Payung Amos
Published: January 2023

Nowadays, the stakes of operating hydrocarbon producing wells in mature field are getting higher. Advanced technologies are needed, and the industry must improve the cost efficiency of maturing assets to compensate for declining production and high fixed costs. One of the methods developed for efficient operation in producing hydrocarbon in mature field is one-phase-well (OPW), which is a well architecture without 9-5/8" surface casing. For safety reason, the conventional pressure temperature fluid analysis (PT-FA) logging can no longer be performed in OPW; alternative approaches are required to determine the potential of reservoirs. Pulsed neutron log (PNL) is proposed as a one-stop-shop solution to determine main reservoir characteristics and fluid status in anticipation of future OPW implementation. Latest generation PNL technology utilizes high-counting neutron generator coupled to high-resolution nuclear detectors to measure accurate oil and gas saturation by means of carbon/oxygen ratio (COR), in-situ total organic carbon, sigma, neutron porosity and novel fast neutron cross section (FNXS) measurements, while simultaneously providing accurate lithology volumes and porosity by means of advanced elemental spectroscopy combined with cased-hole porosity (TPHI).

To evaluate the robustness and applicability of the method, the latest generation PNL was run in three recently drilled wells which have complete open-hole (OH) logs dataset along with fluid analysis (FA) and mudlog information. The PNL data were processed and interpreted independently, without utilizing any input from the OH log data and without the support of fluid analysis and mudlog information. A criterion based on comparison and correlation between fluid volumes and saturation defined by the PNL and the fluid analysis from FA was defined. For all the wells, the PNL interpretation results matched the hydrocarbon information from FA with success ratio higher than 90 percent over the logged interval, confirming its ability to perform accurate standalone evaluation and its value as alternative technology for effective fluid analysis. Because of the complexity of the system, conditions and potential risks must be properly analyzed case-by-case to encourage more massive application in the future.
A Numerical Study of Mineral Dissolution in Deep Heterogeneous Carbonate Reservoirs: Implications for CO2 Geo-sequestration
Authors: Fathy Ahmed , Arif Muhammad , Adila Ahmed Sami , Raza Arshad , Mahmoud Mohamed
Published: January 2023

CO2 geo-sequestration has shown potential to mitigate global warming caused by anthropogenic CO2 emissions. In this context, CO2 can be immobilized in subsurface formations due to chemical dissolution/precipitation via mineral trapping. However, long-term mineralization involves interdependent complexity of dissolution and precipitation kinetics. In this study, a numerical approach is developed and implemented to analyze the effect of rock type, reservoir temperature, brine salinity on CO2 mineral trapping in compositionally distinct subsurface carbonate reservoirs.

Here, we simulated field-scale models for three different subsurface reservoirs’ compositions (calcite, dolomite, and siderite) to assess the mineral trapping capacity. The base case of a 3D carbonate formation was created. The petrophysical parameters were then upscaled (Sw, Sg, K, and φ) to capture the subsurface conditions. Subsequently, CO2 mineral trapping capacity was computed for different rock compositions mimicking carbonate/brine/CO2 systems. Moreover, the CO2 geo-storage potential was assessed under reservoir temperature, salinity, storage duration, and cumulative injected CO2.

The effect of reservoir mineralogy was analyzed via the amount of CO2 mineralized within 100 years of storage duration following 2 years of injection as a base case. The results revealed significant variation in storage capacity as the mineral type changed. In particular, 100% calcite surface showed the highest CO2 storage capacity compared to both dolomite and siderite. The results could be attributed to the distinction of each mineral in terms of its relative cations dissolve-out rate. Moreover, increasing the reservoir temperature resulted in a monotonic increase in mineralization potential with an insignificant increase in case of siderite. Notably, calcite outperformed both siderite and dolomite as a preferable medium for CO2 mineralization as the injection duration increased over both 100 and 200 years of storage. Additionally, the increase in salinity either significantly decreased the amount of CO2 mineralized in case of calcite and siderite or showed no effect at all in case of dolomite.

This work provides a new insight for underpinning the effects of carbonate reservoir composition on CO2 mineral trapping capacity which has not been investigated much. Overall, the results showed that CO2 trapping in subsurface carbonates immobilized CO2 for a long-term stable geo-storage.
CCUS Numerical Simulation Technology and its Application in a Carbonate Reservoir of the Middle East
Authors: Li Qiaoyun , Wu Shuhong , Jia Han , Wang Baohua , Deng Xili , Li Hua , Fan Tianyi , Xu Mingyuan
Published: January 2023

Carbon dioxide Utilization and storage technology (CCUS) plays an important role for oil and gas field to further improve oil and gas recovery and achieve the goal of "double carbon" in recent years. Numerical simulation is an essential means for reservoir engineers to study the flow mechanism of CO2 flooding and storage, screen the CCUS technical indicators, predict the enhanced oil recovery and evaluate storage potential. This paper discusses the multiphase and multicomponent mathematical model based on the seepage theory of carbon dioxide flooding and storage, the calculation method of gas-liquid equilibrium based on EOS equation, the miscibility determination technique and two phase P-T flash calculation method. Meanwhile the CCUS numerical simulator developed based on above theoretical model and calculation method. Applying the simulator to CO2 miscible flooding in a carbonate reservoir of the Middle East, the results show that the model and software accurately describes the CO2-EOR seepage mechanism and CO2 miscible displacement process. It effectively predicts the effect of CO2 injection on EOR and the storage potential after CO2 flooding. It can provide feasible technical guidance for the optimization of CO2 utilization and storage programs.
An Efficient Simulation Approach for Long-term Assessment of CO2 Storage in Complex Geological Formations
Authors: Zhang Ziliang , Wang Yuhang , Vuik Cornelis , Hajibeygi Hadi
Published: January 2023

We present an efficient compositional framework for simulation of CO2 storage in saline aquifers with complex geological geometries during a lifelong injection and migration process. To improve the computation efficiency, the general framework considers the essential hydrodynamic physics, including hysteresis, dissolution and capillarity, by means of parameterized space. The parameterization method translates physical models into parameterized spaces during an offline stage before simulation starts. Among them, the hysteresis behavior of constitutive relations is captured by the surfaces created from bounding and scanning curves, on which relative permeability and capillarity pressure are determined directly with a pair of saturation and turning point values. The new development also allows for simulation of realistic reservoir models with complex geological features. The numerical framework is validated by comparing simulation results obtained from the Cartesian-box and the converted corner-point grids of the same geometry, and it is applied to a field-scale reservoir eventually. For the benchmark problem, the CO2 is injected into a layered formation. Key processes such as accumulation of CO2 under capillarity barriers, gas breakthrough and dissolution, are well captured and agree with the results reported in literature. The roles of various physical effects and their interactions in CO2 trapping are investigated in a realistic reservoir model using the corner-point grid. It is found that dissolution of CO2 in brine occurs when CO2 and brine are in contact. The effect of residual saturation and hysteresis behavior can be captured by the proposed scanning curve surface in a robust way. The existence of capillarity causes less sharp CO2-brine interfaces by enhancing the imbibition of the brine behind the CO2 plume, which also increases the residual trapping. Moreover, the time-dependent characteristics of the trapping amount reveals the different time scales on which various trapping mechanisms (dissolution and residual) operate and the interplay. The novelty of the development is that essential physics for CO2 trapping are considered by the means of parameterized space. As it is implemented on corner-point grid geometries, it casts a promising approach to predict the migration of CO2 plume, and to assess the amount of CO2 trapped by different trapping mechanisms in realistic field-scale reservoirs.
Novel Completion and Stimulation Technologies from Planning to Execution Tripled the Injectivity Index for the First Water Injectors in a Giant Tight Carbonate Reservoir, Onshore Abu Dhabi
Authors: Farnetano Ruben P. , Daoud Ahmed M. , Mogensen Kristian , Fawzy Ahmed , Alnuaimi Eiman , Al Sereidi Bothainah
Published: January 2023

Unit R is a tight carbonate reservoir onshore Abu Dhabi currently undergoing redevelopment with the aim to substantially improve the ultimate recovery factor. Better pressure support is required to increase the production. To this end, two water injectors were drilled in tight areas where wells suffer from low injectivity index and high well head injection pressure (WHIP). Reaching the expected injection rates therefore required very aggressive matrix-acid stimulation techniques.

Acid wash is a common stimulation practice for medium-high permeability wells in ADNOC when the purpose is merely to remove drilling damage without creating large negative skins. For low permeability reservoirs such as Unit R, more aggressive stimulation techniques are needed to deliver the expected production and injection target rates. We tested two such methods; Well A1 was treated using Abrasive Jetting (JA), which is conveyed by coiled tubing whereas in Well A2, we deployed a Smart Liner (SL) where acid can be bullheaded at high rate from surface.

In Well A1, the Abrasive Jet CTU in Open Hole doubled the injectivity index. However, the Injection Logging Test (ILT) revealed an uneven injection profile along the horizontal section (more injection in the better properties zone).

In well A2, Smart Liner bull-head acid stimulation was able to triple the injectivity index. The ILT shown an even injection profile across the horizontal section, actually slightly more in areas with poor rock properties due to a large number of holes in those intervals.

The cost of the Abrasive Jet is 40% less than the Smart Liner stimulation but the latter has more medium/long term advantages:

Even injection along horizontal section (improving sweep efficiency) Ensure 100% accessibility to the well (avoiding formation collapse issue) Stimulation from surface, no need CTU (saving cost), useful for potential future re-stimulations.

After only 10 months of injection the reservoir pressure has increased 50-100 psi in surrounding nearby oil producers, resulting in 20% more oil while honoring all operational guidelines.

It is quite exceptional to be able to assess with real data the effectiveness of two different stimulation techniques. The two pilot wells have same horizontal length, almost same rock properties, and are drilled parallel to each other.

Both Smart Liner and abrasive jetting substantially improve water injectivity but the more uniform flow profile observed in the Smart Liner is a distinct advantage when selecting an appropriate completion-stimulation design for future water injectors.
A Characterization of Tight Sandstone: Effect of Clay Mineralogy on Pore-Framework
Authors: AlKharraa Hamad Salman , Wolf Karl-Heinz A. A. , Kwak Hyung T. , Deshenenkov Ivan S. , AlDuhailan Mohammed A. , Mahmoud Mohamed A. , Arifi Suliman A. , AlQahtani Naif B. , AlQuraishi Abdulrahman A. , Zitha Pacelli L. J.
Published: January 2023

Macro-, meso-, micro-pore systems combined with clay content are critical for fluid flow behavior in tight sandstone formations. This study investigates the impact of clay mineralogy on pore systems in tight rocks. Three outcrop samples were selected based on their comparative petrophysical parameters (Bandera, Kentucky, and Scioto). Our experiments carried out to study the impact of clay content on micro-pore systems in tight sandstone reservoirs involve the following techniques: Routine core analysis (RCA), to estimate the main petrophysical parameters such as porosity and permeability, X-ray diffraction (XRD), and scanning electron microscopy (SEM) to assess mineralogy and elemental composition, Mercury Injection Capillary Pressure (MICP), Nuclear Magnetic Resonance (NMR), and Micro-Computed Tomography (Micro-CT) to analyze pore size distributions. Clay structure results show the presence of booklets of kaolinite and platelets to filamentous shapes of illite. The Scioto sample exhibits a micro-pore system with an average pore body size of 12.6±0.6 μm and an average pore throat size of 0.25±0.19 μm. In Bandera and Kentucky samples illite shows pore-bridging clay filling with an average mineral size of around 0.25±0.03 μm, which reduces the micro-pore throat system sizes. In addition, pore-filling kaolinite minerals with a diameter of 5.1±0.21 μm, also reduce the micro-pore body sizes. This study qualifies and quantifies the relationship of clay content with primary petrophysical properties of three tight sandstones. The results help to advance procedures for planning oil recovery and CO2 sequestration in tight sandstone reservoirs.
Developing a Generalized Correlation to Obtain Steady-State Based Permeability Using a Probe Permeameter
Authors: Abdullah Eassa , Baatiyah Mobarak , Aljabri Jaber , Alfaraj Mashaer , Al-Otaibi Mohammed , ElHusseiny Ammar , Alade Olalekan
Published: January 2023

The importance of permeability cannot be under-stimated. It is used in crucial equations used to determine quantities analysed by reservoir, drilling, and production engineers. Using permeability along with other properties is important to understanding reservoir behaviour when wells are drilled, to calculate the rate of the fluid flow, as illustrated by Darcy's equation that relates fluid flow to permeability. Measuring permeability in the laboratory with the conventioanl available steady-state equipment can be time consuming, especially if it was done by gas injection which requires measuring at different pressure points to satisfy Klinkenberg equation. A quick measuring equipment called the prob permeameter have been used for many years, it quantitatively performs a permeability point measurement as a function of position on either a whole core, slabbed core or a rock slab. However, despite of its prompt and easy measurement, most of the results represents a general idea about the actual permeability and sometimes even falls out of the range, which makes it unrelaiable. Series of experiments were conducted for a variety of rock samples with a wide range of permeability ranging from tight to permeable, to compare the generated results between both of the above equipment. The results were graphed and been compared using different point of views, mathematicalwise, petroleum engineeringwise, and geologicalwise. Ultimately, an equation to correlate between the results was developed graphically and using logistic regression techniques.
Accounting for Regional Aquifer Data in a Simulation Model of the Burgan Minagish Reservoir – A New Approach
Authors: Al-Faresi Fahad , Monfared Hashem , Evro Solomon , Ali Sher , Madhavan Sethu , Yaser Muhammad , Yunusov Manuchehr
Published: January 2023

Burgan Minagish (BGMN) reservoir in the Greater Burgan field, is one of the reservoirs among others that has been producing from Minagish formation. It has been in production intermittently since 1960's though full development was delayed due to high H2S content and the need to construct sour processing surface facilities.

The analysis of reservoir pressure over time indicated that the main reason of reservoir pressure decline in BGMN was production from three neighboring reservoirs producing from the same formation. This was clear as pressure decline continued for the period of 2005-2009 while Burgan-Minagish was not in production. Reservoir pressure dropped below bubble point in 2011 and made it necessary to design and implement a pressure maintenance program. This included a water injection scheme which has been implemented since 2018.

In 2012, using a novel approach (Al-Faresi et al, 2012), two Fast and Slow Loop simulation models were built to model a regional aquifer and quantify flux exchange between Burgan-Minagish, regional aquifer and surrounding reservoirs. While the Fast Loop coarse model was used to match field level production and pressure data, the detailed BGMN Slow Loop model would solely focus on modeling of BGMN having incorporated the impact of neighboring reservoirs via calculated flux derived from Fast Loop model. The drawback, however, is that every time the model needs updating, a tedious and manual process must be performed to calculate flux rate across the BGMN boundaries from the Fast Loop and then incorporate it explicitly into the Slow Loop model.

10 years after the previous study, a new approach for regional aquifer modeling has been utilized to integrate two existing simulation models in a combined one that serves for both purpose of modeling the regional aquifer and surrounding reservoirs as well as history matching of high resolution BGMN model. The history matched combined model has been used to extensively evaluate BGMN reservoir production performance under different development scenarios. We have compared the results with existing two-model solution, and it is clear from the analysis that the impact of neighboring reservoirs as well as the regional aquifer is captured in the new approach.

The novelty of the new approach is that it allows a seamless interaction between three surrounding reservoirs with BGMN. The unified simulation model is more convenient to use and eliminates the manual work of influx calculations. It is also more flexible and repeatable as systematic updates on both static and dynamic modeling sides for any reservoir in the area of interest could be easily implemented.
A Hybrid Neural Workflow for Optimal Water-Alternating-Gas Flooding
Authors: Singh Gurpreet , Davudov Davud , Al-Shalabi Emad W. , Malkov Anton , Venkatraman Ashwin , Mansour Ahmed , Abdul-Rahman Rosemawati , Das Barun
Published: January 2023

Water-alternating-gas (WAG) injection is a gas-based enhanced oil recovery (EOR) technique used to overcome problems related with gas injection including gravity override, viscous fingering, and channeling. The WAG EOR technique is used to control gas mobility, which boosts project economics. Water alternating gas (WAG) has the dual benefit of higher recovery than continuous gas injection and CO2 sequestration. Higher sweep efficiencies and conformance control have been shown to increase the life cycle net present value (NPV) for improved field development and deployment planning. Nevertheless, a poor WAG design often results in unfavorable oil recovery. This study investigates WAG optimization in a sandstone field using a hybrid numerical-machine learning (ML) model. In this work, we present a hybrid neural approach for optimizing the WAG injection process that can be easily integrated as a workflow with any existing reservoir simulator for optimal WAG parameters to maximize reservoir life cycle cumulative recoveries.

The reservoir simulator is treated as a sample generator to form an ensemble of recovery scenarios with the WAG parameters as inputs to a dense neural network (DNN) and outputs/labels as cumulative recoveries. The neural network then serves two roles: 1) a readily available map between WAG parameters and cumulative recoveries for reduced computational cost and hence faster on-demand evaluation, and 2) as a repository condensing important correlations that can be appended with additional samples or reduced by removing redundant samples (simulation runs). Consequently, the hybrid neural approach also provides a clear picture of which simulation runs (or samples) are more conducive to optimal recovery predictions for an effective strategy to sample the high dimensional WAG parameter space and reduced compute times. This becomes especially important when we consider field scale optimization scenarios with multiple wells each with their separate injection schedules requiring exponentially increasing samples with a brute force ensemble approach (add an example in the introduction section or later and cross-refer here).
Using Isotope Technology to Identify Oil and Gas Reservoir Sweet Spots
Authors: Lu Feng Hu
Published: January 2023

Porosity and permeability of rocks are crucial parameters utilized to assess the quality of oil and gas reservoirs. Diagenesis in carbonate reservoirs commonly results in dissolution, and creates secondary porosity and permeability. At the same time, geochemical records (e.g., isotopes and elements) in the carbonate rocks are diagenetically altered. This study is to utilize the isotope technology to evaluate diagenesis and its impact on oil and gas reservoirs. Micro-samples were collected from marine carbonate rocks in a studied reservoir for carbon and oxygen isotope analyses, plus analyses of Sr isotopes and element concentrations. The analytical results show outstanding negative shifts of oxygen isotopes in some intervals, whereas carbon and strontium isotopes have a minor or little change. These intervals also contain lower element contents including strontium. It is believed that these intervals experienced diagenetic dissolution with abundant secondary porosity, and are the best potential for oil and gas reservoirs. Petrographic study indicates minor cements but higher porosity developed within these intervals. This is confirmed by high porosity measured within these intervals by using other methods including gas injection and well logging. Furthermore, the degrees of water-rock interaction were quantitively assessed by modeling covariations of isotope pairs, which can help evaluate the quality of reservoirs relating to the intensity of diagenesis. Intensive diagenesis with a high water-rock ratio particularly in a closed system may result in additional cementation, alteration of most geochemical parameters, and also reduce porosity and permeability. Thus, the isotope technology can be utilized as an effective method to assess reservoir potential and determine reservoir sweet spots. Note that carbonate cementation and recrystallization commonly occur during diagenesis, and commonly show negative oxygen isotope values. If sampling happens to contain a certain amount of cements and recrystallized carbonate minerals, the analytical results will show lower oxygen isotopes and trace elements than their primary values in the studied marine carbonates. Petrographic study was conducted to help micro-sampling, and verify that cements were not collected in the studied carbonate rocks for isotope analysis. The implementation of this study is that the isotope results obtained from one studied well can be applied in the whole field or/and the region, as diagenesis rarely occurs only around one well. This will save cost for repeatedly measurements from different wells and fields by using other methods.
Vertical Composition Variation with Multiple Samples for Reservoir Model Initialization
Authors: Droubi Nour El , Tahir Sofiane , Ghorayeb Kassem , Su Shi , Assaf Georges , Ramatullayev Samat , Kloucha Chakib Kada , Mustapha Hussein
Published: January 2023

A challenging step in reservoir modeling is capturing fluid composition variation. This is a complex task as fluid samples taken from wells in different areas of the reservoir usually have large areal and vertical compositional variation. Modeling representative composition variation with depth in the presence of multiple samples is critical for reservoir simulation and hydrocarbon initially in place assessment, and, on the other hand, a technically challenging task. In this paper, we present an automated workflow integrated in a commercial exploration and production (E&P) software that addresses compositional variation for reservoir simulation model initialization for multiple fluid samples.

Composition variation with depth requires a depth window, number of depth points, composition, temperature, pressure and reference depth for all fluid samples. Using a specific equation of state (EoS), the workflow is executed for every fluid sample by performing compositional variation with depth based on Gibbs conditions for thermodynamic equilibrium. The output of this step is a composition variation with depth distribution for every fluid sample. Finally, the best-matching model is chosen by comparing each model results with the data for all existing fluid samples.

The proposed workflow was tested using a specific EoS in a reservoir with several fluid samples. One composition variation with depth model was generated for every fluid sample. In the next step, all models were evaluated by calculating the average errors between the model and each fluid sample. Finally, the best-matching models were selected, and the results were evaluated. It was observed that the best-matching models were able to accurately predict the pressure and saturation pressure for large number of fluid samples. The proposed workflow was also integrated into an industry-leading E&P modeling software platform to serve as an automated functionality that outputs the required files to perform initialization with equilibration of the dynamic reservoir model.

Capturing fluid composition variation in the reservoir is an important step in reservoir modeling. The proposed work presents an automated workflow that generates the best-matching composition variation with depth model for multiple samples. Using traditional approaches, this is a challenging and time-consuming step as fluid samples taken from wells in different areas of a reservoir can have significant areal compositional variation.
Full Field Karst Features Characterization Using Reflection Seismic Attributes and Calibration with Diffraction Seismic in a Giant Offshore Carbonate Field, UAE
Authors: Ahmad Jamal , Shekhar Ravi , Mohamed Naeema Ahmed
Published: January 2023

Identifying key geological features (e.g. karsts, faults, fractures) are extremely important for well planning and mitigating drilling hazards. To identify and characterize these features, a high-resolution diffraction study was conducted in a pilot area which successfully detected and mapped subtle geological features. Subsequently, existence of these karst/cave like features in the field also assist in disposing produced water as two produced water disposal wells were successfully drilled. The field is expected to double water production in next 10 years therefore targeting these karst features is expected to improve disposal well performance

Application of diffraction imaging in a pilot area distinctively improved spatial resolution in order to enhance the detections of the edges and confirmed high-resolution subsurface imagining compared to conventional reflection seismic. However, extending diffraction study to full field is costly, computationally extensive and require unique skill set. Hence, an attempt was made to exploit conventional seismic reflection data utilizing advanced seismic attributes (e.g. spectral decomposition) to generate high-resolution subsurface image comparable to diffraction images. Appropriate frequencies were carefully chosen and color-blended to produce such high-resolution image that confidently enhance karst collapse features distribution in the full field.

Wells drilled within karsts collapse features identified from diffraction data shows two-fold water disposal increment compared to non-karst areas. Dynamic data (e.g. mud losses & PLT) supports result from diffraction seismic in the pilot area. Subsequent full field feasibility study was conducted exploiting available conventional reflection seismic data to produce similar high-resolution subsurface image. With optimum parameters selection in spectral decomposition, subsurface color-blended image produced using reflection seismic shows good correlation with diffraction seismic results within pilot area and improves confidence in imaging outside the pilot area. Results are successfully calibrated with diffraction seismic and other available dynamic data. Distribution of full field Karst collapse features are outlined confidently, which are helpful to improve future well planning and mitigating drilling hazards.

One of the main challenges in the carbonate environment is to map karsts collapse and other subtle features due to their complex shapes and lack of resolution in post stack seismic. No doubt cost inhibited diffraction seismic study produced high-resolution subsurface image however, it was just an application in a small pilot area within a giant field. Integrated workflow presented in this paper save not only outsourcing cost but also produce similar results by utilizing available conventional reflection seismic data.
Combining Production Logging with Spectral Noise Logging and High Precision Temperature to Detect Wellbore and Behind Pipe Fractures Fluid Movement
Authors: Alabdulmuhsin Ali , Ashby Scott , Terentyeva Elena , Ammar Karim
Published: January 2023

Diagnosing fractures and behind-pipe isolation is very critical to analyze production from unintended reservoirs correctly and taking proper actions. Fracture flow diagnostics using through-barrier diagnostic system is a unique solution developed to identify the positions and heights of behind pipe fractures in addition to confirming the isolation integrity between the stages.

In combination with Production logging, Spectral Noise Log (SNL) & High Precision Temperature (HPT) tool string have been running in-memory mode. Combining the technologies allowed for analyzing wellbore flow profiles by production logging. Simultaneously, SNL-HPT platform is diagnosed behind Pipe fracture flow and zonal isolations.

A well was drilled targeting three stages. After three perforation and fracturing stages, water cut increase was observed. Understanding fracture network behind pipe and across the perforations, analyzing zonal isolation, and finding true flow geometry on the "well–formation" system is now the critical objective of the diagnostics survey.

SNL-HPT platform under fracture flow diagnostics results, reveal behind pipe conditions accurate picture. Actual positions of active fractures behind and across each perforation stage and active reservoir zones of the non-fractured stage are correctly identified. As well isolations conditions between stages are determined and flow geometry inside the borehole and behind the pipe is provided.

By combining the SNL-HPT platform diagnostics with production logging results, the source of the high water cut caused by fracture extension was successfully identified. Isolation's decisions are properly taken and well set for production.
Full-GPU Simulation of Coupled Multi-Reservoir Models: Implementation and Field Examples
Authors: Mukundakrishan Karthik , Rosa Andrea , Ranjan Saurabh , Panfili Paola , Cinquini Fabrizio , Cominelli Alberto , Patacchini Leonardo , Esler Kenneth
Published: January 2023

Graphics Processing Units (GPUs) have been successful in providing up to an order of magnitude performance benefit for reservoir simulation; this has been demonstrated for both standalone black-oil and compositional models. To our knowledge however, a demonstration of the applicability of GPUs to the simulation of models coupled at the asset level is still missing.

In this paper, we present how a full-GPU reservoir simulator has been turned into a field simulator whereby multiple reservoirs can be coupled by a field scheduler, using the Message Passing Interface (MPI). The default scenario is to use one (or possibly multiple) GPUs per reservoir. As an alternative, it is possible to leverage NVIDIA's Multi Process Service (MPS) to run the full asset on a single GPU when the reservoir models aggregate can fit in memory, resulting in optimal usage of available computing resources.

The field scheduler may integrate reservoir models using three different logic in the definition of the coupling frequency: (a) periodic, (b) at each time-step, or (c) at each Newton iteration. Smoothness of production and injection profiles typically improves from (a) to (c), while the simulation cost can either increase or decrease depending on the model specifics.

Using a set of case studies based on real industrial problems, we demonstrate the robustness and performance of the implementation. We also discuss the impact on resources utilization in a typical computational environment used by reservoir engineers, in particular the possibility to run probabilistic forecast workflows on a limited number of cluster nodes. This paper presents for the first time the effectiveness and efficiency of a GPU based reservoir simulator for integrated asset models consisting of many reservoirs connected to a common surface gathering system. The implementation offers the capability to fully exploit the memory bandwidth and the fine grain parallelism offered by modern GPUs to perform complex simulations, providing further evidence that leading-edge high-performance systems, typically designed by energy companies for seismic processing, are also suitable for reservoir simulation.
Deep-Learning-Based Surrogate Model to Predict CO2 Saturation Front in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach
Authors: Tariq Zeeshan , Xu Zhen , Gudala Manojkumar , Yan Bicheng , Sun Shuyu
Published: January 2023

Naturally fractured reservoirs (NFRs), such as fractured carbonate reservoirs, are ubiquitous across the worldwide and are potentially very good source to store carbondioxide (CO2) for a longer period of time. The simulation models are great tool to assess the potential and understanding the physics behind CO2-brine interaction in subsurface reservoirs. Simulating the behavior of fluid flow in NFR reservoirs during CO2 are computationally expensive because of the multiple reasons such as highly-fractured and heterogeneous nature of the rock, fast propagation of CO2 plume in the fracture network, and high capillary contrast between matrix and fractures. This paper presents a data-driven deep learning surrogate modeling approach that can accurately and efficiently capture the temporal-spatial dynamics of CO2 saturation plumes during injection and post-injection monitoring periods of Geological Carbon Sequestration (GCS) operations in NFRs. We have built a physics-based numerical simulation model to simulate the process of CO2 injection in a naturally fractured deep saline aquifers. A standalone package was developed to couple the discrete fracture network in a fully compositional numerical simulation model. Then reservoir model was sampled using the Latin-Hypercube approach to account for a wide range of petrophysical, geological, reservoir, and operational parameters. The simulation model parameters were obtained from extensive geological surveys published in literature. These samples generated a massive physics-informed database (about 900 simulations) that provides sufficient training dataset for the Deep Learning surrogate models. Average Absolute Percentage Error (AAPE) and coefficient of determination (R2) were used as error metrics to evaluate the performance of the surrogate models. The developed workflow showed superior performance by giving AAPE less than 5% and R2 more than 0.95 between ground truth and predictions of the state variables. The proposed Deep Learning framework provides an innovative approach to track CO2 plume in a fractured carbonate reservoir and can be used as a quick assessment tool to evaluate the long term feasibility of CO2 movement in fractured carbonate medium.
Numerical Investigation of Hybrid Smart Water and Foam Injections in Carbonate Reservoirs
Authors: Hassan Anas Mohammed , Tackie-Otoo Bennet N. , Ayoub Mohammed A. , Mohyaldinn Mysara E. , Al-Shalabi Emad W. , Adel Imad A.
Published: January 2023

This contribution is a progressive effort to investigate the effect of the novel hybrid EOR method of Smart Water Assisted Foam (SWAF) technique on oil recovery from carbonates through numerical modeling. In this work, a core-scale model was utilized to provide an insight and a better understanding of the controlling mechanisms behind incremental oil recovery using a new hybrid EOR method consisting of a combination of smart water flooding and foam injection, termed as Smart Water Assisted Foam (SWAF) technology, particularly for carbonate reservoirs. A core-scale model encapsulating the physics of SWAF flooding was used to history-match experimental data and the model was further optimized utilizing the CMG simulator. For extracting the most value from this numerical investigation, a sensitivity analysis was performed to monitor the effect of influential parameters affecting oil recovery depending on the spectrum of the experimental data available. The objective functions used in the sensitivity analysis include minimizing the history-matching global error and maximizing the oil recovery profiles. Three sensitivity analysis approaches were used: Tornado-plot, SOBOL analysis, and MORRIS analysis. For generating the related proxy models, polynomial regression, and radial basis function (RBF) neural networks were investigated. Subsequently, the DECE-based and PSO-based optimization methods were employed to examine the effect of chemical design parameters such as smart water (Mg2+), surfactant aqueous solution (SAS), and foam concentrations along with the liquid production rate on the oil recovery factor during SWAF-flooding.

Based on the numerical results, the experimental coreflooding data were accurately history-matched using the proposed model with a minimal error of 4.74% applying the PSO-based optimization method. Furthermore, in terms of the objective function prediction during the sensitivity analysis study, the comparative assessment of both proxy models on the verification plot reveals that the RBF neural network outperforms the polynomial regression. Consolidated findings from the three sensitivity analyses, i.e., the Tornado-plot, SOBOL, and MORRIS, outline three common parameters that significantly affect the oil recovery profiles that are liquid production rate (LigProdCon), foam (DTRAPW SAS2), and Mg2+ concentration (DTRAP Mg3) parameters. On the other hand, in terms of maximizing the oil recovery while minimizing the usage of injected chemicals during SWAF flooding, the optimal solution via the PSO-based approach is superior (97.89%) to the DECE-based optimal solutions (92.47%). This work presents one of the few studies investigating the numerical modeling of the SWAF process and capturing its effects on oil recovery. The optimized core scale model can be further used as a base for building a field-scale model and designing a successful pilot project.
Advanced Porosity and Permeability Analyses Based on BHI Approach
Authors: AL Isaee Omar Mohammed , Smirnov Dmitrii , AL Hadhrami Abdullah , AL Shabibi Hilal , Khayrutdinov Fanis , Al Jahwari Fatma Khamis , AL Raisi Muatasam Hassan , Al-Maawali Ibrahim Saleh
Published: January 2023

Carbonate intrasalt stringers formation evaluation proved to be complex due to the nature of the rock lithology through syndolomitization and postdolomitization process. The need for the radioactive data is highly recommended, however different degrees of depletion along the reservoir units are posing clear threat to data acquisition. This study aims to establish an advanced model of calculating the porosity and permeability using non-radioactive BHI approach without compromising with the quality of the formation evaluation.

This study was conducted while facing the challenge of limited data acquisition with the pressure behavior across the carbonates stringers and the need to understand the nature of the permeability disturbance. The pilot covers two wells, Well-A was most useful regarding the source of the data for the analyses. Missing information was obtained from Well-B, which consist of the same dataset. The resistivity image data quality throughout the interpreted BHI section was good. Several analyses were carried out to characterise BHI data including fractures interpretation, porosity prediction (microfractures, intragranular pores and fluid inclusions) and permeability distribution (fracture aperture analysis).

Based on this study, the following observations are obtained, the predicted porosity and permeability are well correlated with the core and density – neutron data. The net pay identification and porosity calculation is consistent with those derived from density-neutron logs. Highly fracture zones were detected across the reservoir, which explain the permeability disturbance. Natural fracture tends to be found in clusters throughout the borehole. Conductive fracture, which has direct influence to the permeability were interpreted using the electrical borehole image to estimate the electrical apertures of open fractures around the wells. Pressure behavior across the reservoir might be linked to the natural fracture networks (conductive and non-conductive fractures). In the development side, this study helps to have better understanding of the rock typing and Lithotypes associated with different permeability clusters and reservoir quality integrated with production behavior.

The developed methodology of BHI evaluation provides reliable results in Porosity and Permeability evaluation. The model integrates relevant disciplines to evaluate intrasalt stringers dolomite and limestone mixed environment through independent lithology approach to eliminate the effect of the matrix heterogeneity. In addition, BHI approach provides safer logging acquisition and better option for advanced natural fracture analysis.
Velocity Model Building for Depth Imaging of Carbonate Reservoirs to the Deep Salt Incorporating Walkaway VSP and Gravity-Magnetic Data From OBC Survey Offshore Abu Dhabi
Authors: Waqas Muhammad , Hou Lian , Prieux Vincent , Meffre Adrien , Sabetian Raffaela , Prigent Hervé , Alkaabi Ahmed Saeed , van Kleef Franciscus , Jan Kamran Saeed , Alkobaisi Abdulla Saad , Aljaberi Salem Mubarak
Published: January 2023

Use of depth imaging is increasing day by day in green as well as in brown fields. Velocity model is constructed by integration of multiple available datasets and techniques. Depth imaging based on a high-resolution velocity model constrained by wells resulted in improved image of the overburden as well as carbonate reservoirs to the deep salt using 3D OBC seismic, borehole seismic and Gravity-Magnetic data from two adjacent offshore fields in Abu Dhabi.

Multi-Wave Inversion (MWI) utilizing direct arrivals, surface waves and two-way time surfaces was applied to obtain high-resolution velocity model in the near surface validated by checkshots and sonic logs. Near surface velocity inversions were detected by MWI which improved overall gather flatness in the near surface.

The current stress regime as well as the network of faults resulted in HTI anisotropy in the dome area, which is visible on the multi-azimuthal Walkaway VSP travel-time residuals from the observed and modelled data. HTI is also visible on azimuthal move-out in Common Offset Common Azimuth (COCA) gathers. HTI tomography was tested, giving equally flat gathers achieved by azimuthal move-out correction.

A Gravity-Magnetic survey covering the area was inverted and integrated for the deep salt and basement model. The basement was constrained by the results from magnetic depth estimation. Density model of deep salt sediments and basement was enhanced by Gravity-Magnetic inversion. The resulting density model was converted to velocity which was then incorporated in the final velocity model followed by tomographic update. Final imaging provided a better stacking response and improved gather flatness on the salt dome.

High-resolution velocity model provided improved imaging, well ties and depth conversion. Improved AVO/AVA response helped patrial angles stacks for improved reservoir characterization. Improved imaging in the deep section was also achieved.
Real Time Artificial Intelligence Assisted Operations Geology: Lithology and Fluids Early Identification and Automatic Well Correlation
Authors: Di Palo Arcangelo , Pianaro Stefano , Crottini Ada , Tempone Pamela , Sanasi Carla
Published: January 2023

During the drilling of a well, a huge quantity of data is acquired in real-time. In order too mitigate risks due to geological uncertainties, to increase operational efficiency, to optimize processes and create new business models, Eni has developed its own cross-functional integrated data platform, which ensures data availability to all subsurface technical functions sharing a common data model. In this paper we describe an innovative approach, born from the collaboration between expert geologists and data scientists. The integrated team has developed a tool based on Artificial Intelligence (AI) supporting operations geologist during drilling phases. Two different tools have been created: litho-fluid interpretations, a set of AI algorithms used to identify in real-time the lithology and to interpret the formation fluids; well-to-well log correlation and look ahead, models used to find analogies between intervals of the well being drilled and the reference well, allowing to estimate the distance and time of arrival to a given geological event.

The results obtained have been remarkable in terms of accuracy. The positive feedbacks from the operations geologists give the assurance of the usefulness of the tools and their expected benefits: the tools allow to better control geological uncertainties and speed up some repetitive and time-consuming tasks. The results presented in this paper are focused on two UAE applications of litho-fluid and well-to-well log correlations.