bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using Isotope Technology to Identify Oil and Gas Reservoir Sweet Spots
Authors: Lu Feng Hu 
Published: January 2023

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.