Fine Scale Simulation of Fractured Reservoirs: Applications and Comparison

Imperial Users onl

Impact of Heterogeneity on Production in Tidal Sandstone Reservoirs: Application to the Linnorm Field, Offshore Norway

Imperial Users onl

Neural network applications to reservoirs: Physics-based models and data models

International audienceEditoria

Modelling of Multiphase Fluid flow in Heterogeneous Reservoirs

Computational modeling of multiphase fluid flow in highly heterogenous problems with complicated geometries is a challenging problem for reservoir engineers, with a rich research in establishing best methods and approaches. The novelty in this work is centered around the implementation and comparison of simulation results from two software - the open source ICFESRT and the commercial software ECLIPSE - for a two-phase multiphase problem (oilwater) in both simple and complex geometries. The work involves: (a) implementation and comparison of simulation results from the two software on three different, hypothetical but typical geometries; (b) consideration of a real field case and the associated data analysis, rock characterization, and geostatistics of a real field representative of a highly heterogeneous reservoir; and (c) implementation of both software on the real field case for predictions of oil production at the site, and comparison of the simulation results from the two software. The initial comparison of simulation results for was carried out using three hypothetical (but common) geometries, these being: (a) a quarter five spot with one geological layer; (b) the same geometry as in (a) but with a vertical heterogeneity i.e. 5 different geological layers; (c) and lastly a full 5 spot with 5 different geological layers was implemented. Three different mesh resolutions were applied in both software and comparisons were carried out for mesh-independency. The results showed that in all these three scenarios, good agreement was observed between IC-FERST (coarse mesh) and ECLIPSE (fine mesh) with an average percentage difference at the production well ranging between 2.5% and 10.5% for the oil production and 12% and 26% for the water production. Both the ICFERST and ECLIPSE were subsequently implemented on a real, heterogeneous field – which consisted of 25 producing wells and 8 injections wells. Prior to the software implementation, a data analysis and rock characterization was carried out –Using data from the 33 wells. The logging and core data (a total of 30,000 log readings and 1150 core samples) were utilized and a novel rock characterization technique -Balaha Rock Characterization Code- was implemented to allow for the optimal clustering of rock types within the reservoir, The rock characterization resulted in identifying 7 rock types with their unique porosity-hydraulic permeability relationships. Subsequently, geostatistical methods were implemented – which enabled populating the computational cells of the two software with the corresponding reservoir properties (porosity, hydraulic permeability). To achieve the property population into the unstructured computational domain of the ICFERST software, a newly-developed script was written in Matlab and Python. The rock properties data populated on IC-FERST consist of porosity, permeability, relative permeability, capillary pressure and connate water saturation. A further comparison between the IC-FERST simulation results with the corresponding ECLIPSE simulations was carried out – were all simulations were carried out for a period of 40 years. The percentage differences between the two software simulations were estimated for : (i) ten individual production wells and (ii) the total of all production wells. The results showed that a good agreement exists between the IC-FERST and ECLIPSE simulations, with an average percentage difference for the total oil production of 10.5%, the total water production of 26% and the total water injection of 14%. The results for the ten individual wells showed an average percentage difference of 15.5% ranging from 3 to 29% for the oil production in the late time period. Slightly higher differences were observed when the overall period was considered, due to the large difference at the early time period of the simulation. The results indicated that IC-FERST, when incorporating the necessary rock characterization information – which highlight the heterogeneity of the reservoir – can produce results that can compete with the industry standard ECLIPSE. Additional aspects need to be considered within the current real field IC-FERST simulation, the inclusion of possible fractures and faults, as these were incorporated in the computational domain of ECLIPSE. Additional capabilities also still need to be embedded into IC-FERST, such as the incorporation of the fluid density and viscosity variations with pressure and the consideration of the volume factors, in order to enhance its competitiveness with existing commercial reservoirs simulators such as ECLIPSE

Auto-detection interpretation model for horizontal oil wells using pressure transient responses

Directional drilling is an excellent option to extend the limited reservoir reach and contact offered by vertical wells. Pressure transient responses (PTR) of horizontal wells provide key information about the reservoirs drilled. In this study multilayer perceptron (MLP) neural networks are used to correctly identify reservoir models from pressure derivative curves derived from horizontal wells. To this end, 2560 pressure derivative curves for six distinct reservoir models are generated and used to design a machine-learning classifier. A single hidden layer MLP network with 5 neurons, trained with a scaled conjugate gradient algorithm, is selected as the best classifier. This smart classifier provides total classification accuracy of 98.3%, mean square error of 0.00725, and coefficient of determination of 0.97332 over the whole dataset. Performance accuracy of the proposed classifier is verified with real field data, synthetically generated noisy PTR, and some signals outside the range initially assessed by the training plus testing data subsets. The developed network can correctly identify the reservoir-flow model with a probability of close to 0.9. The novelty of this work is that it employs a large dataset of horizontal (not vertical) well tests applied to six reservoir-flow models and includes noisy data to train and verify a neural network model to reliably achieve a high-level of prediction accuracy.CIted as: Moosavi, S.R., Vaferi, B., Wood, D.A. Auto-detection interpretation model for horizontal oil wells using pressure transient responses. Advances in Geo-Energy Research, 2020, 4(3): 305-316, doi: 10.46690/ager.2020.03.08    

Impact of anisotropy and fracture density on the approximation of the effective permeability of a fractured rock mass using 2D models

Imperial Users onl

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done

Great SCO2T! Rapid tool for carbon sequestration science, engineering, and economics

CO2 capture and storage (CCS) technology is likely to be widely deployed in coming decades in response to major climate and economics drivers: CCS is part of every clean energy pathway that limits global warming to 2C or less and receives significant CO2 tax credits in the United States. These drivers are likely to stimulate capture, transport, and storage of hundreds of millions or billions of tonnes of CO2 annually. A key part of the CCS puzzle will be identifying and characterizing suitable storage sites for vast amounts of CO2. We introduce a new software tool called SCO2T (Sequestration of CO2 Tool, pronounced "Scott") to rapidly characterizing saline storage reservoirs. The tool is designed to rapidly screen hundreds of thousands of reservoirs, perform sensitivity and uncertainty analyses, and link sequestration engineering (injection rates, reservoir capacities, plume dimensions) to sequestration economics (costs constructed from around 70 separate economic inputs). We describe the novel science developments supporting SCO2T including a new approach to estimating CO2 injection rates and CO2 plume dimensions as well as key advances linking sequestration engineering with economics. Next, we perform a sensitivity and uncertainty analysis of geology combinations (including formation depth, thickness, permeability, porosity, and temperature) to understand the impact on carbon sequestration. Through the sensitivity analysis we show that increasing depth and permeability both can lead to increased CO2 injection rates, increased storage potential, and reduced costs, while increasing porosity reduces costs without impacting the injection rate (CO2 is injected at a constant pressure in all cases) by increasing the reservoir capacity.Comment: CO2 capture and storage; carbon sequestration; reduced-order modeling; climate change; economic