Improved Simulation Efficiency for Optimising Inflow Control Device Completions Using Suitably Upscaled Models and Parallel Global Search Optimisation Techniques

Inflow Control Device (ICD) completions can improve well performance by adjusting the inflow profile along the well and reducing the influx of unwanted fluids. The ultimate aim of using ICD completions is to provide maximum oil recovery and/or Net Present Value (NPV) over the life of the field. Proactive ICD optimisation studies use complex reservoir models and high-dimensional nonlinear objective functions to find the optimum ICD configurations over the life of the field. These complex models are generated from fine scale detailed geological models to accurately capture fluid flow behaviour in the reservoir. Although these high-resolution geological models can provide better performance predictions, their simulation runtimes can be computationally expensive and time consuming for performing proactive ICD optimisation studies that often require thousands of simulation runs. We propose a new workflow where we use upscaled and locally refined models coupled with parallelised global search optimisation techniques to improve the simulation efficiency when performing ICD optimisation and decision-making studies. Our approach preserves the flow behaviour in the reservoir and maintains the interaction between the reservoir and the well in the near wellbore region. Moreover, when coupled with parallel optimisation techniques, the simulation time is significantly reduced. We present an in-house code that couples global search optimisation algorithms (Genetic Algorithm and Surrogate Algorithm) with a commercial reservoir simulator to drive the ICD configurations. We evaluate the NPV as the objective function to determine the optimum ICD configurations. We apply and benchmark our approach to two different reservoir models to compare and analyse its efficiency and the optimisation results. Our analysis shows that our proposed approach reduces the run time by more than 80% when using the upscaled models and the parallel optimisation techniques. These results were based on a standard dual-core parallel desktop configuration. Additional results also showed further reduction in run time is possible when employing more processors. Additionally, when testing different ICD completion strategies (ICDs in producers only, ICDs in injectors only, and ICDs in both producers and injectors), the NPV can be increased by 9.6% for the optimised ICD completions. The novelty of our work is rooted in the much-improved simulation efficiency and better performance predictions that supports ICD optimisation and decision-making studies during field development planning to maximize profit and minimize risk over the life of the field.

Spatial agents for geological surface modelling

Increased availability and use of 3D-rendered geological models have provided society with predictive capabilities, supporting natural resource assessments, hazard awareness, and infrastructure development. The Geological Survey of Canada, along with other such institutions, has been trying to standardize and operationalize this modelling practice. Knowing what is in the subsurface, however, is not an easy exercise, especially when it is difficult or impossible to sample at greater depths. Existing approaches for creating 3D geological models involve developing surface components that represent spatial geological features, horizons, faults, and folds, and then assembling them into a framework model as context for downstream property modelling applications (e.g. geophysical inversions, thermo-mechanical simulations, and fracture density models). The current challenge is to develop geologically reasonable starting framework models from regions with sparser data when we have more complicated geology. This study explores the problem of geological data sparsity and presents a new approach that may be useful to open up the logjam in modelling the more challenging terrains using an agent-based approach. Semi-autonomous software entities called spatial agents can be programmed to perform spatial and property interrogation functions, estimations and construction operations for simple graphical objects, that may be usable in building 3D geological surfaces. These surfaces form the building blocks from which full geological and topological models are built and may be useful in sparse-data environments, where ancillary or a priori information is available. Critical in developing natural domain models is the use of gradient information. Increasing the density of spatial gradient information (fabric dips, fold plunges, and local or regional trends) from geologic feature orientations (planar and linear) is the key to more accurate geologic modelling and is core to the functions of spatial agents presented herein. This study, for the first time, examines the potential use of spatial agents to increase gradient constraints in the context of the Loop project (https://loop3d.github.io/, last access: 1 October 2021​​​​​​​) in which new complementary methods are being developed for modelling complex geology for regional applications. The spatial agent codes presented may act to densify and supplement gradient as well as on-contact control points used in LoopStructural (https://www.github.com/Loop3d/LoopStructural, last access: 1 October 2021) and Map2Loop (https://doi.org/10.5281/zenodo.4288476, de Rose et al., 2020). Spatial agents are used to represent common geological data constraints, such as interface locations and gradient geometry, and simple but topologically consistent triangulated meshes. Spatial agents can potentially be used to develop surfaces that conform to reasonable geological patterns of interest, provided that they are embedded with behaviours that are reflective of the knowledge of their geological environment. Initially, this would involve detecting simple geological constraints: locations, trajectories, and trends of geological interfaces. Local and global eigenvectors enable spatial continuity estimates, which can reflect geological trends, with rotational bias, using a quaternion implementation. Spatial interpolation of structural geology orientation data with spatial agents employs a range of simple nearest-neighbour to inverse-distance-weighted (IDW) and quaternion-based spherical linear rotation interpolation (SLERP) schemes. This simulation environment implemented in NetLogo 3D is potentially useful for complex-geology–sparse-data environments where extension, projection, and propagation functions are needed to create more realistic geological forms.

Comparison of various lithology volumes building methods: a case study of the Bejil field

The Bejil field case study shows the comparison of 3D lithology volumes built by various methods, applied world-wide. A net-reservoir volume is an important and integral part of 3D geological models, which determines the oilnet pay part of the reservoir. The quality of the geological model directly affects the concept of the studied geological environment. Two lithology volume options are considered in detail. The first method, which is more popular in domestic applications, involves building a lithology volume directly based on logging data interpretations. The second method, which is more widespread internationally, involves building a volume of facies environments followed by distributing various lithotypes in a reservoir taking into account the facies structural features. As a result, we made allowance for the tasks and geological features of the field and chose the best modeling method.

The Influence of Lithofacies Features of a Deposit on the Efficiency of Reserves Recovery

The main task in petroleum engineering is to achieve the maximum possible production of hydrocarbon reserves with low expenditures. Many reasons influence the economics of the project. And one of them is related to choosing the right location for drilling a well in order to produce unrecovered hydrocarbons. The choice of this place has a direct correlation with the geological aspects of an oil field. This paper showed that different facies have a great influence on reserves recovery on the example of the South-Vyintoyskoye field. The classification of facies involved the study of production data. This study was presented by analysis of core sample, application of models by Muromtsev, reading of well logs, and build-up of geological models. The construction of geological models involved work in IRAP RMS TEMPEST, CorelDRAW, Geoglobe, Roxar Program package. The subject of study was the reservoir rock BV7/3-4that is a part of the Barremian age formation. It was concluded that this reservoir rock is composed of mainly argillaceous sandstones, interbedded with siltstones and shales. Authors identified that this formation belongs to three different depositional facies. The facies classification showed that the A1 zone is associated with well-graded fine to the fine-grained size of sediments. The reservoir rock of this zone is composed of sediments that belong to the distribution channel of deltas. And as a result, it is characterized by its high flow rate of production wells. The reservoir rock of the A2 facies zone is composed of sediments that are related to the collapse of mouth bars and branches of deltas. This zone has difficulties in fluid migration due to the presence of heterogeneities and clay material as well as the drilling of new wells close to the given zone lead to the medium flow rate of production wells. The reservoir rock of the A3 facies zone is composed of sediments that belong to turbidity flows. In this zone, organic matter has been recognized as a group of ichnofacies such as "Cruziana". This zone is characterized by the low flow rates of the production wells.

River sedimentation modeling using ground-penetrating radar

In recent years, siltation has become quite a problem. It has been the main cause of flooding and a rapid decline in water quality. It is usually caused by a high river sedimentation rate and/or uncontrolled waste disposal. The increased rate of erosion also means that river sedimentation occurs faster than normal and could lead to environmental hazards, wildlife deaths, and the disruption of food and drinking water supply among other things. The question is how to monitor the sedimentation process of rivers without damaging the river itself. The suitable geophysical method is GPR. GPR is an active, non-intrusive geophysical method in which electromagnetic radiation and the reflected signals in the form of radar pulses are used for subsurface imaging. The objective is to investigate river sedimentation using GPR, we created the synthetic models based on geological models of rivers with different depths to create their 2-D radargrams to predict the actual model. We set up the first model RSM-I as control which consists of a layer of freshwater with ρ = 16 Ωm, k = 81 and μ r = 1 of depth 5 m, two layers of sandstone with ρ = 850 Ωm, k = 2.5 and μ r = 1 of total depth 4 m, and a layer of claystone with ρ = 120 Ωm, k = 11 and μ r = 1 of depth 1 m. RSM-II and III are added with a buildup of saturated sediment with ρ = 30 Ωm, k = 15, and μ r = 1 of depth 2.5 and 4 m, respectively. The radargrams’ reflector for each model shows a two-way travel time of 300-350, 150-200, and 60-90 ns in their respective order. GPR models can differentiate between the saturated sediment and freshwater, it shows good results regarding sediment investigation in rivers.

Into the Noddyverse: A massive data store of 3D geological models for Machine Learning & inversion applications

Unlike some other well-known challenges such as facial recognition, where Machine Learning and Inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled datasets that can be used to validate or train robust Machine Learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test datasets are often not particularly geological, nor geologically diverse. To overcome these limitations, we have used the Noddy modelling platform to generate one million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic datasets. This model suite can be used to train Machine Learning systems, and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite, and discuss the opportunities such a model suit affords, as well as its limitations, and how we can grow and access this resource.

Conditioning surface-based geological models to well data using artificial neural networks

Surface-based modelling provides a computationally efficient approach for generating geometrically realistic representations of heterogeneity in reservoir models. However, conditioning Surface-Based Geological Models (SBGMs) to well data can be challenging because it is an ill-posed inverse problem with spatially distributed parameters. To aid fast and efficient conditioning, we use here SBGMs that model geometries using parametric, grid-free surfaces that require few parameters to represent even realistic geological architectures. A neural network is trained to learn the underlying process of generating SBGMs by learning the relationship between the parametrized SBGM inputs and the resulting facies identified at well locations. To condition the SBGM to these observed data, inverse modelling of the SBGM inputs is achieved by replacing the forward model with the pre-trained neural network and optimizing the network inputs using the back-propagation technique applied in training the neural network. An analysis of the uncertainties associated with the conditioned realisations demonstrates the applicability of the approach for evaluating spatial variations in geological heterogeneity away from control data in reservoir modelling. This approach for generating geologically plausible models that are calibrated with observed well data could also be extended to other geological modelling techniques such as object- and process-based modelling.