3D GPR attributes to generate classified facies models: A case study from a dune island

Ground-penetrating radar (GPR) is a standard geophysical technique to image near-surface structures in sedimentary environments. In such environments, GPR data acquisition and processing are increasingly following 3D strategies. However, the processed GPR data volumes are typically still interpreted using selected 2D slices and manual concepts such as GPR facies analyses. In seismic volume interpretation, the application of (semi-)automated and reproducible approaches such as 3D attribute analyses as well as the production of attribute-based facies models are common practice today. In contrast, the field of 3D GPR attribute analyses and corresponding facies models is largely untapped. We develop and apply a workflow to produce 3D attribute-based GPR facies models comprising the dominant sedimentary reflection patterns in a GPR volume which images complex sandy structures on the dune island of Spiekeroog (Northern Germany). After presenting our field site and details regarding our data acquisition and processing, we calculate and filter 3D texture attributes to generate a database comprising the dominant texture features of our GPR data. Then, we perform a dimensionality reduction of this database to obtain meta texture attributes, which we analyze and integrate using composite imaging and (also considering additional geometric information) fuzzy c-means cluster analysis resulting in a classified GPR facies model. Considering our facies model and a corresponding GPR facies chart, we interpret our GPR data set in terms of near-surface sedimentary units, the corresponding depositional environments, and the recent formation history at our field site. Thus, we demonstrate the potential of the proposed workflow, which represents a novel and clear strategy to perform a more objective and consistent interpretation of 3D GPR data collected across different sedimentary environments.

The Impact of Facies Variability within the Harwich Formation on Ground Engineering in the London Area, UK.

The Eocene Harwich Formation, underlying the Greater London (UK) area, presents many construction problems for design and location of tunnels, pipelines, and other engineering infrastructure projects. Variable deposits make up the sequence of the Harwich Formation. These include cemented fault zones, hard grounds, loose gravel and sand that, when unexpectedly encountered, can cause construction delays and increase costs. Here, we interpret borehole cores and logs, in-situ observations coupled with borehole derived samples, and calculate particle-size distributions to develop a general facies model that accounts for the lithological distribution within the Harwich Formation. This provides an improved geological framework for proposed subsurface construction that can reduce inherent engineering uncertainties, not only in the London region, but potentially in other similar geological environments.

The turbidite-contourite-tidalite-baroclinite-hybridite problem: orthodoxy vs. empirical evidence behind the “Bouma Sequence”

The underpinning problems of deep-water facies still remain unresolved. (1) The Tb, Tc, and Td divisions of the turbidite facies model, with traction structures, are an integral part of the “Bouma Sequence” (Ta, Tb, Tc, Td, Te). However, deposits of thermohaline contour currents, wind-driven bottom currents, deep-marine tidal currents, and baroclinic currents (internal waves and tides) also develop discrete rippled units, mimicking Tc. (2) The application of “cut-out” logic of sequences, which was originally introduced for the “Bouma Sequence”, with sharp basal contacts and sandy divisions containing well-developed traction structures, to muddy contorts with gradational basal contacts and an absence of well-developed traction structures is incongruent. (3) The presence of five internal divisions and hiatus in the muddy contoured facies model is in dispute. (4) Intersection of along slope contour currents with down slope sediment-gravity flows, triggering hybrid flows, also develops traction structures. (5) The comparison of genuine hybrid flows with down slope flow transformation of gravity flows is inconsistent with etymology of the term “hybrid”. (6) A reexamination of the Annot Sandstone at the Peira Cava type locality in SE France fails to validate either the orthodoxy of five internal divisions of the “Bouma Sequence” or their origin by turbidity currents. For example, the “Ta” division is composed of amalgamated units with inverse grading and floating mudstone clasts, suggesting a mass-transport deposit (MTD). The “Tb” and “Tc” divisions are composed of double mud layers and sigmoidal cross bedding, respectively, which suggest a tidalite origin. (7) Although it was reasonable to introduce a simplistic “Bouma Sequence” in 1962, at a time of limited knowledge on deep-water processes, it is obsolete now in 2021 to apply this model to the rock record amid a wealth of new knowledge. (8) The disconnect between 12 observed, but questionable, modern turbidity currents and over 10,000 interpreted ancient turbidites defies the doctrine of uniformitarianism. This disconnect is attributed to routine application of genetic facies models, without a pragmatic interpretation of empirical data. (9) A suggested solution to these problems is to interpret traction structures in the sedimentary record pragmatically on the basis of empirical field and experimental evidence, without any built-in bias using facies models, such as the “Bouma Sequence”. (10) Until reliable criteria are developed to distinguish traction structures of each type of bottom currents based on uniformitarianism, a general term “BCRS” (i.e., bottom-current reworked sands) is appropriate for deposits of all four kinds of bottom currents.

Dissolution Facies Window Identification of Carbonate Gas Reservoir by Using Electrical Image Logs: A Case Study from Sichuan Basin

Predicted reservoir results from conventional methods didn’t match the production performance in GS B well block in the Lower Sinian Dengying dolomite formation. The predicted gas production of vertical well is around 500k m3/day, but the real gas production is below 100k m3/day. In GS A well block, the predicted gas production of vertical well is consistent with the real gas production around 500k m3/day, and when meter cavie develops, test gas production can reach 1000k m3/day. It suggests the biggest challenge is to clarify reservoir characterization in GS B well block. However, due to the limited resolution of conventional logs and strong heterogeneity of carbonate reservoir, conventional open hole logs and seismic data has limitation to provide the details of secondary pore and fractures to clarify reservoir characterization. The electrical image logs provide high resolution images with high borehole coverage. It can provide abundant information about secondary pore and fracture to identify dominant dissolution facies window. Through electrical image logs, secondary pore and fracture classification in 50 vertical wells were performed in the Lower Sinian Dengying dolomite formation. Five facies were detected based on electrical image logs, including vug facies (honeycomb vug facies, algal stromatolite vug facies and bedding vug facies), cave facies, fracture-vug facies, massive dense facies and dark thin layer dense facies. With the five facies and top interface constraints from seismic data, 3D dissolution facies model was created, which can show different dissolution facies window of GS A and GS B well block. The method in this paper reveals the reason of confliction and agree test gas production. The case study presents how to identify five dissolution facies based on high-resolution electrical image logs with core data calibration. Besides, 3D dissolution facies model is created to show dissolution facies window of GS B well block to optimize well trajectory deployment during the development stage. Better understanding of reservoir characterization was instructive for acid fracturing design of Dengying dolomite gas reservoir as well.

Reservoir facies design and modeling using probabilistic rock-physics templates

We have developed a technique to design and optimize reservoir lithofluid facies based on probabilistic rock-physics templates. Subjectivity is promoted to design possible facies scenarios with different pore-fluid conditions, and quantitative simulations and evaluations are conducted in facies model selection. This method aims to provide guidelines for reservoir-facies modeling in an exploration setting in which limited data exist. The work includes two parts: facies-model simulations and uncertainty evaluations. We have first derived scenarios with all possible fluid types using Gassmann fluid substitution. We designed models with different numbers of facies and pore-fluid conditions using site-specific rock-physics templates. Detailed facies simulations were conducted in the petroelastic, elastic, and seismic domains in a step-by-step framework to preserve the geologic interpretability. The use of probabilistic rock-physics templates allowed for multiple realizations of each facies model to account for different types and magnitudes of errors and to infer facies probability and uncertainty. For each realization, we used Bayesian classification to assign facies labels. Comparisons between the predicted and true labels provided the success rates and entropy indices to quantify the prediction errors and confidence degrees, respectively. This workflow was tested with well-log data from a clastic reservoir in the Gulf of Mexico. We simulated models with five to seven facies with different pore-fluid parameters. From the petroelastic, elastic, and seismic domains, the uncertainty of facies models significantly increased due to well-log measurement errors, data-model mismatch, and resolution differences. The facies model consisting of oil sand, gas sand, and shale was the optimal set based on the high success rates and low entropy indices. Facies profiles estimated from this optimal model presented significant consistency with well-log interpretations. The techniques and results demonstrated here could be applied to different types of clastic reservoirs, and they provide useful constraints for reservoir facies modeling during early oilfield exploration stages.