Carbonate rocks are complex in their structures and pore geometries and often exhibit a challenge in their classification and behavior. Many rock properties remain unexplained and uncertain because of improper characterization and lack of data QC. The main objective of this paper is to study flow behavior of relative permeability with different rock types in complex carbonate reservoirs.
Representative core samples were selected from two major hydrocarbon reservoirs in Abu Dhabi. Rock types were identified based on textural facies, PoroPerm characteristics and capillary pressure. Porosity ranged from 15% to 25%, while permeability varied from 1 mD to 50 mD. Primary drainage and imbibition water-oil relative permeability (Kr) curves were measured by the steady-state technique using live fluids at full reservoir conditions with in-situ saturation monitoring. High-rate bump floods were designed at the end of the flooding cycles to counter capillary end effects. Aging period of 4 weeks was incorporated at the end of the drainage cycle. Robust data QC was performed on the samples, and final validation of the relative permeability was conducted by numerical simulation of the raw data and measured capillary pressure.
The followed QC procedure was crucial to eliminate artefact in the relative permeability curves for proper data evaluation. The different rock types showed consistent variations in the relative permeability hysteresis and end points. Imbibition relative permeability curves showed large variations within the different rock types, where Corey exponent to oil ‘no’ increased with permeability from 3 to 5, whereas the Corey exponent to water ‘nw’ decreased with permeability and ranged from 3 to 1.5. The variations in the relative permeability curves are argued to be the result of different rock structures and pore geometries. Variations were also seen in the end-point data and showed consistent behavior with the rock types.
The different carbonate rock types were identified based on geological and petrophysical properties. Higher permeability samples were grain-dominated and more heterogeneous in comparison to the lower permeability samples, which were mud-dominated rock types. Imbibition Kr curves showed larger variations than the primary drainage data, which cannot be interpreted based on wettability considerations only. The relative permeability curves have been thoroughly evaluated and QC'd based on raw data of pressure and saturation by use of numerical simulation. Such RRT-based Kr data are not abundant in the literature, and hence should serve as an important piece of information in mixed-wet carbonate reservoirs.
Yuan, Chengdong (Southwest Petroleum University) | Pu, Wanfen (Kazan Federal University) | Varfolomeev, Mikhail A. (Southwest Petroleum University) | Wei, Junnan (Kazan Federal University) | Zhao, Shuai (Southwest Petroleum University) | Cao, Li-Na (Kazan Federal University)
Conformance control treatment in high-temperature and ultra-high-salinity reservoirs for easing water/gas channeling through high-permeability zones has been a great challenge. In this work, we propose a deformable micro-gel that can be used at more than 100 °C and ultra-high salinity (TDS > 200000 mg/L, Ca2+ + Mg2+ > 10000 mg/L), and present a method for choosing the suitable particle size of micro-gel to achieve an optimal match with the pore throat of core.
First, the particle size distribution of micro-gel was analyzed to decide d50, d10 and d90 (diameter when cumulative frequency is 50%, 10% and 90%, respectively). Core flooding experiments were conducted under different permeability conditions from 20 to 900 mD. The migration and plugging patterns of the micro-gel were studied by analyzing and fitting injection pressure curves together with the change in the morphology of produced micro-gel analyzed by microscope. Finally, a quantitative matching relation was established between the size of micro-gel particles and the pore-throat size of core, its effectiveness was verified by evaluating plugging ability in subsequent water injection process.
The migration and plugging patterns were divided into three patterns: complete plugging, plugging – passing through in a deformation or broken state – deep migration, and inefficient plugging – smoothly passing through – stable flow. The second pattern can be further divided into three sub-patterns as strong plugging, general plugging and weak plugging. Based on the five patterns, a quantitative matching relation between the size of micro-gel particles and the pore-throat size of cores was established by defining three matching coefficients α=d10/d, β=d50/d, γ=d90/d (d is the average pore throat diameter). The effectiveness of this quantitative matching relation was verified by evaluating the plugging ability (residual resistance coefficient) in sequent water flooding process after the injection of 1.5 pore volume of micro-gel. For a strong permeability heterogeneity, the strong plugging is believed to be the expected pattern. The particles size and the pore-throat size should meet the following relationship: 1 < α < 2, 2 < β < 4, 4 < γ <6. In this scenario, the deformable micro-gel particles could achieve both an effective plugging and a deep migration. The quantitative matching relation can provide an indication for the quick determination of the suitable size of deformable micro-gel for conformance control processes in field application, including profile control and water-shut off treatment.
The characterization of the clastic Zubair reservoir is challenging because of the high lamination and the oil properties change making the conventional saturation technique uncertain. A new workflow has been recently established in the newly appraised wells which has involved advanced petrophysical measurements along with the fluid sampling. The new technique has led to identify new HC layers that were overlooked by the previous techniques, thus adding more reserves to the KOC asset.
Because of the high lamination of clastic Zubair formation and the change of the oil properties, the dielectric dispersion measurement was integrated along with the diffusion-based NMR to identify new oil zones that has been initially masked by the resistivity-based approach. The new approach has also provided details on the oil movability and the characterization of its property. As the newly identified layers were identified for the 1st time across the field, the fluid sampling was conducted to confirm the new findings.
The advent of a new logging technology from a multi-frequency dielectric technique deployed over the formation has independently pinned down the HC pays over the Zubair interval, including a new zone below the water column. The zone was initially identified as heavy Tar zone. The advanced diffusion-based NMR was thus conducted and integrated with Dielectrics which has demonstrated the movability of HC using the diffusion-based NMR approach over the newly identified zone. A fluid sampling was later performed which has confirmed the new finding. The new identified zone was initially overlooked by the previous interpretation and extensive modeling over the entire field. The seal mechanism was also explained by taking advantage of the high-resolution dielectric dispersion measurement (mainly the low frequency), which has been also supported by the Images interpretation. This new approach has added an incremental oil storage over the field.
As the main gas producing blocks of South China Sea, Y field group has four gas fields on operation and two gas fields on going. In production process, there is water influx, sand, carbon dioxide rich, high pressure, high temperature, limitations of the decarbonization ability, and hydrocarbon components content requirement of downstream users. This paper focuses on establishing the integrated model of reservoir, well, gathering network and provides forecasting schemas for meeting contract requirements.
This paper addressed the Y-gas filed group challenges and methodology of flow behavior from reservoir to pipeline, using the Petroleum Experts' Integrated Production Model suite of software (IPM). The integrated model lays special stress on high temperature, multi-layer IPR, liquid loading, CO2 component change. Model calibrations include the characteristics of water vaporization condensation, getting multi-layer IPR though iterating MBAL model and multi-phase flow model, analyzing rules between cumulative gas production rate and component CO2, N2.
Based on the objective gas field group integrated model, the production forecasting and gas supply optimization are studied to solve the problem of multi-system and multi-constraints while conventional research are difficult to achieve the global objective optimization. According to forecasting results, the contract requirements of 4.251 billion cubic meters can be met by 2025. Due to production decline, the gas field group has been unable to meet the contract since 2026. The terminal decarbonization capacity can fully meet the needs. 13-2 gas field is a high proportion hydrocarbon gas reservoir, and hydrocarbon gas volume of the gas field group could meet the contract demand during the whole production forecast period. Some preliminary results from this optimization are also presented.
In the future, the integrated model of simulation and optimization can be carried out in combination with other possible conditions. The objectives and constraints can be adjusted according to the change of production conditions, that optimize the development plans and submit the optimization results.
Vahrenkamp, Volker (King Abdullah University of Science and Technology) | Khanna, Pankaj (King Abdullah University of Science and Technology) | Petrovic, Alexander (King Abdullah University of Science and Technology) | Ramdani, Ahmad (King Abdullah University of Science and Technology) | Putri, Indah (King Abdullah University of Science and Technology) | Sorrentino, Ranglys (King Abdullah University of Science and Technology)
The characterization and modelling of carbonate reservoirs can still be significantly improved to account for complex property and fracture network heterogeneities at scales difficult to resolve in the subsurface. The objective of this research is to develop and establish workflows for high fidelity geological modelling and characterization using modern and ancient carbonate outcrop analogues.
As a first step, we carefully selected high quality modern and ancient analogues to create comprehensive data sets on depositional heterogeneities. Advanced instrumentation and techniques were used such as 3D drone surveys, high-resolution surface geophysical surveys (50 MHz-100 MHz, and seismic), chirp sub- bottom profiler and high-resolution bathymetry mapping. These high-end techniques are paired with tried and tested standard geological techniques of measuring stratigraphic sections anchored by outcrop spectral gamma ray logs, analysis of sediment samples (texture, grain size, mineralogy, geochemistry) and fracture/fault surveys all integrated with full cores drilled in the outcrops. Using these, data models can be created for depositional and fracture heterogeneities at different scales and populated with ranges of property data like those found in actual reservoirs. The outcome will be a series of models for various carbonate reservoir settings and well location patterns with the goal of supporting drilling/exploration operations and reducing future development costs.
The project is based on two large-scale research projects of Jurassic carbonates outcropping in central KSA and a large modern carbonate platform in the Red Sea. Jurassic outcrops were analyzed using a unique dataset of measured sections including spectral gamma ray logs (300 vertical m), drone photogrammetry data (4x4 km2 overflight and several km's of vertical cliffs), seismic data (2 km), and GPR data (8 km). Data expose lateral heterogeneities, facies dimensions, and fracture networks at different scales. The modern carbonate outcrops are an ideal laboratory to investigate lateral facies heterogeneities and their relation to environmental factors influencing sediment distribution (prevailing winds versus storms, climate and nutrients). Around 800 km of hydroacoustic data, 50 sediment cores and 200 sea-floor samples were collected exposing significant and complex heterogeneities.
The outcome of these research projects significantly increases our understanding of property heterogeneity, facies distribution, fracture networks, and architecture of complex carbonate reservoirs. Resulting multi-scale modelling approaches and associated facies templates will improve the prediction of spatial heterogeneities of facies in subsurface reservoirs of similar settings. In addition, these datasets can be used as input for static analogue models and dynamic simulations to test sensitivities and determine optimum development scenarios for improving ultimate recovery.
In this study, we aim to demonstrate how machine learning can empower computational models that can predict the flow rate of a given well. Given current real-time data and periodic well tests, this new method computes flow rates using data-driven model. The computational model is based on analyzing the relations and trends in historical data. Relational databases include huge amounts of data that have been accumulated throughout decades. In addition, there is a large number of incoming operational data points every second that gives a lot of insight about the current status, performance, and health of many wells. The project aims to utilize this data to predict the flow rate of a given well.
A variety of well attributes serve as inputs to the computational models that find the current flow rate. Artificial Neural Networks (ANN) were used in order to build these computational models. In addition, a grid search algorithm was used to fine-tune the parameters for the ANN for every single well. Building a single unique model for every well yielded the most accurate results. Wells that are data-rich performed better than wells with insufficient data. To further enhance the accuracy of the models, models are retrained after every incoming patch of real-time data. This retraining calibrates the models to constantly represent the true well performance and predict better. In practice, Flow rate prediction is used by production engineers to analyze the performance of a given well and to accelerate the process of well test verification. One of the main challenges in building unique models for every well is fine-tuning the parameters for the artificial neural networks, which can be a computationally intensive task. Parameter fine-tuning hasn't been discussed in previous literature regarding flow rate prediction. Therefore, our unique approach addresses the individuality of every well and builds models accordingly. This high-level of customization addresses the problem of under-fitting in ANN well models.
The Eocene C-Sup VLG3676 reservoir is one of the most important oil reservoirs of Western Venezuela. It has a high lateral sedimentological heterogeneity, some sand production issues due to low sandstone cohesion and high stress gradients, and asphaltene precipitation problems. This has lead to the construction of a compositional/geomechanical model in order to design a palliative strategy. The proposed modeling methodology includes nine phases: 1) Development of the 3D mechanical earth model (MEM) to simulate reservoir compaction; 2) Quality control of the static model, including the relative permeability values; 3) Development of a fluid model that predicts the onset of asphaltene precipitation; 4) Development of a rock-fluid interaction model; 5) Initialization and calibration of the compositional model; 6) Coupling of the compositional and geomechanics models; 7) History matching; 8) Analytical estimation of the onset of sand production; 9) Implementation of an opportunity index analysis for asphaltene precipitation and sand production. Based on this compositional/geomechanical model it has been possible to map the risk of asphaltene precipitation and sand production in the reservoir, therefore showing that such problems are critical in the Eocene Misoa C-2-Sup and C-3-Sup stratigraphic units, due to the energy depletion caused by the production.
Su, Qin (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Zeng, Huahui (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Zhang, Xiaomei (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Lv, Lei (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Qie, Shuhai (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Meng, Huijie (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina)
With the continuous development of oil and gas exploration technology, the remaining exploration targets in the middle and shallow areas of the land are becoming less and less, and the deep complex targets have become an important replacement area for oil and gas growth. In order to enhance the deep tight gas exploration potential of the Songliao Basin in China, structural interpretation and reservoir prediction of deep volcanic rocks and glutenite lithologic gas reservoirs are carried out, while the basic requirements for seismic data acquisition in complex reservoir exploration in the middle-deep layers are: higher sampling density, even distribution of space, appropriate offset. In the face of particularly complex reservoirs, it is necessary to fully strengthen the acquisition parameters, ensure the reservoir prediction needs, and avoid the waste caused by the inability to solve the geological problems. However, due to the weak signal and strong interference, the conventional narrowazimuth three-dimensional observation system in the Songliao Basin is affected by factors such as low folds, large grid bin, and low reception of complex structural information, which affects the middledeep layers. The imaging effect has restricted the development of tight gas exploration in the middledeep layers. Therefore, the broadband, wide-azimuth and high-density (BWH) 3D seismic exploration technology has been developed. BWH refers to a wider excitation and reception frequency band, a wider reception orientation, and a higher sampling density. Generally, broadband acquisition requires signals with an octave of more than 5 times; wide-azimuth observation systems should have an aspect ratio greater than 0.5, where an aspect ratio greater than 0.85 is called an omnidirectional observation system; when using an explosive source, sampling densities greater than 500,000 channels/km
Many application and operational methods have been developed for applying carbonate matrix acidizing to successfully stimulate heterogeneous and long horizontal openhole zones. These methods have also been implemented during acid fracturing to various degrees of success. This paper discusses in detail the laboratory assessment of a biodegradable material for acid diversion in highly fractured formations.
Diversion in fracture acidizing is extremely challenging because of the high pumping rate, extreme pressures, and larger volumes of acid compared to matrix acidizing. To effectively stimulate natural or pre-existing fractured formations, the diverting agent should be able to bridge not only at the perforations, but inside the fracture system, too. Historically, several methods have been implemented for acid-fracturing diversion, such as ball sealers, viscous fluids, packers, etc., resulting in limited success in formations with natural or pre-existing fractures. This paper discusses the use of an acid diverter that consists of biodegradable particles with different sizes and hardness. The particle size ratios are specifically designed where large particles will bridge in the fractures while the smaller particles "nest" in the pore throat of the bridged larger particles. This leads to quick, efficient blockage of fractures and acid diversion.
The laboratory assessment of this biodegradable material was conducted at various temperatures up to 300°F and consists of (1) degradation in 3% KCl, live 15 wt% HCl, and spent 15% HCl, and (2) fluid loss using slotted disks at different diverter concentrations. The fractures were mimicked in the laboratory using a stainless steel slotted disk in a high-pressure/high-temperature (HP/HT) cell.
The dissolution rate of the particles was observed to be a function of time and temperature. The dissolution rate of the diverter was higher in water as compared to 15 wt% HCl acid. The stability of the biodegradable diverter was conducted at 300°F. The filter cake was stable up to 30 minutes when 1.0 ppt of the biodegradable diverter was used.
The results of this study indicate that the biodegradable diversion material can be used as an effective alternative diversion method to seal natural or pre-existing fractures.
Core & Log Neural Network Modeling (CLONNE) has been initiated to utilize an ANN to optimize usage of available data to generate synthetic logs and core data which enable user to eliminate any special logs and core data acquisition in the future. This will reduce the well cost and time required for data acquisition and data analysis.
CLONNE process starts with data gathering of the available core and log data which then QC'ed and conditioned for bad hole, light hydrocarbon, thin lamination and normalized. Then pair of core and log data are combined as dummy well to generate the first CLONNE model that can be used to predict for the whole fields. Conventional data including density, neutron, sonic, GR logs and other parameters are used to generate output. A random well from the field is selected to test the predictability matching of CLONNE versus the real data acquired. Several calibration performed to provide the best predictability.
Currently a number of CLONNE models have been created for offshore fields in Malaysia. For CLONNE Synthetic logs, 4 models have been created to predict Porosity, Bulk Density, Neutron and Shear Slowness. For CLONNE Synthetic core, 3 models have been created to predict Grain Size, Permeability and Porosity. All of this models have managed to predict quite well in both thick sand and laminated sand. More models will come to predict other log curves and core parameters. The models established has been tested in one field, where a synthetic sonic log has been created. After the drilling and subsequent logging run, an actual sonic log has been deployed and compared which yield to 96% comparable. The data predicted from CLONNE can greatly save almost 15 months spend to acquire and analyze core data and also almost RM 6 Million total expenditure to acquire and analyze core data.
In 2018, CLONNE has achieved RM 6 Million cost avoidance from application in 3 fields in Malaysia. The CLONNE model generated can be implement to Basin wide prediction thus enable the sharing use of data. This will help to integrate the data available instead of data being utilize in the specific field only.