Two adjacent reservoirs in offshore oil fields have been evaluated using extensive data acquisition across multiple disciplines; several surprising observations were made. Differing levels of biodegradation were measured in the nearly adjacent reservoirs, yet related standard geochemical markers are contradictory. Unexpectedly, the more biodegraded oil had less asphaltene content, and this reservoir had some heavy end deposition in the core but upstructure, not at the oil-water contact (OWC) as would be expected, especially with biodegradation. Wax appears to be an issue in the nonbiodegraded oil. These many puzzling observations, along with unclear connectivity, gave rise to uncertainties about field development planning. Combined petroleum systems and reservoir fluid geodynamic considerations resolved the observations into a single, self-consistent geo-scenario, the co-evolution of reservoir rock and fluids in geologic time. A spill-fill sequence of trap filling with biodegradation helps explain differences in biodegradation and wax content. A subsequent, recent charge of condensate, stacked in one fault block and mixed in the target oil reservoir in the second fault block, explains conflicting metrics of biodegradation between C7 vs. C16 indices. Asphaltene instability and deposition at the upstructure contact between the condensate and black oil, and the motion of this contact during condensate charge, explain heavy end deposition in core. Moreover, this process accounts for asphaltene dilution and depletion in the corresponding oil. Downhole fluid analysis (DFA) asphaltene gradients and variations in geochemical markers with seismic imaging clarify likely connectivity in these reservoirs. The geo-scenario provides a benchmark of comparison for all types of reservoir data and readily projects into production concerns. The initial apparent puzzles of this oil field have been resolved with a robust understanding of the corresponding reservoirs and development strategies.
Electrofacies using well logs play a vital role in reservoir characterization. Often, they are sorted into clusters according to the self-similarity of input logs and do not capture the known underlying physical process. In this paper, we propose an unsupervised clustering algorithm based on the concept of dynamic programming, in which the underlying physical processes and geological constraints, such as the number of clusters, number of transitions between clusters, and minimal size of formation layers, can be directly integrated. We benchmark the proposed algorithm with synthetic data sets and demonstrate its applications to two field examples, where formations are clustered into zones through automated clustering using a consistent resistivity response. The inputs for our examples are porosity, clay volume fraction from elemental analysis, invaded zone resistivity, and invaded zone water saturation from dielectric interpretation or nuclear magnetic resonance logs. The proposed algorithm provides the optimized cluster pattern/electrofacies that satisfies desired constraints and enables the extraction of relevant petrophysical parameters, such as brine resistivity, cementation, and saturation exponents, as well as parameters that relate to the cation exchange capacity (CEC) of the clay for shaly-sand formations. Beyond the immediate examples demonstrated in this paper, we present the proposed algorithm in a generic form such that it can be easily tailored to the task at hand, taking into account any prior knowledge of the physics of the underlying process.
The computation of permeability is vital for reservoir characterization because it is a key parameter in the reservoir models used for estimating and optimizing hydrocarbon production. Permeability is routinely predicted as a correlation from near-wellbore formation properties measured through wireline logs. Several such correlations, namely Schlumberger-Doll Research (SDR) permeability and Timur-Coates permeability models using nuclear magnetic resonance (NMR) measurements, K-lambda using mineralogy, and other variants, have often been used, with moderate success. In addition to permeability, the determination of the uncertainties, both epistemic (model) and aleatoric (data), are important for interpreting variations in the predictions of the reservoir models. In this paper, we demonstrate a novel dual deep neural network framework encompassing a Bayesian neural network (BNN) and an artificial neural network (ANN) for determining accurate permeability values along with associated uncertainties. Deep-learning techniques have been shown to be effective for regression problems but quantifying the uncertainty of their predictions and separating them into the epistemic and aleatoric fractions is still considered challenging. This is especially vital for petrophysical answer products because these algorithms need the ability to flag data from new geological formations that the model was not trained on as “out of distribution” and assign them higher uncertainty. Additionally, the model outputs need sensitivity to heteroscedastic aleatoric noise in the feature space arising due to tool and geological origins. Reducing these uncertainties is key to designing intelligent logging tools and applications, such as automated log interpretation. In this paper, we train a BNN with NMR and mineralogy data to determine permeability with associated epistemic uncertainty, obtained by determining the posterior weight distributions of the network by using variational inference. This provides us the ability to differentiate in- and out-of-distribution predictions, thereby identifying the suitability of the trained models for application in new geological formations. The errors in the prediction of the BNN are fed into a second ANN trained to correlate the predicted uncertainty to the error of the first BNN. Both networks are trained simultaneously and therefore optimized together to estimate permeability and associated uncertainty. The machine-learning permeability model is trained on a “ground-truth” core database and demonstrates considerable improvement over traditional SDR and Timur-Coates permeability models on wells from the Ivar Aasen Field. We also demonstrate the value of information (VOI) of different logging measurements by replacing the logs with their median values from nearby wells and studying the increase in the mean square errors.
The Cretaceous Cape Vulture prospect (Norwegian Sea, Norway) consisted of three Cretaceous sand levels: Cape Vulture Lower, Main, and Upper. The prospect was drilled in 2017, targeting seismic amplitude anomalies that represented a combination of reservoir facies and hydrocarbons. As the first well (6608/10-17S) proved hydrocarbons down to base reservoir in Cape Vulture Main and Upper, an appraisal well with two sidetracks were planned and drilled to determine the reservoir development, pressure communication and oil-water contact. A good understanding of the lateral variation within the reservoir was of importance to the technical economical evaluation of the discovery. The appraisal wells planned for a comprehensive coring and logging program. The main objectives were to reduce the uncertainty of estimated in place volumes by establishing the depth of the hydrocarbon-water contact, prove lateral pressure communication within each reservoir level, reduce the uncertainty of lateral and vertical reservoir distribution and quality, reduce the uncertainty of hydrocarbon saturation and understand the relationship between seismic amplitude anomalies and subsurface properties / fluids. The logging program included triaxial resistivity, nuclear spectroscopy, electrical images, nuclear magnetic resonance (NMR) complementing triple combo, followed by formation pressure measurements, and fluid sampling. The presence of clay minerals in varying amounts within the reservoirs depresses the resistivity measurement and leads to underestimation of the hydrocarbon saturation when using conventional Archie’s equation - a common petrophysical challenge in such conditions. The hydrocarbon saturation is an important parameter when calculating reserves and estimating whether a discovery is of commercial value. Hence, reducing the uncertainty span on hydrocarbon saturation (total and effective) and estimating the net pay thickness is critical. Using core data and advanced down-hole measurements to optimize a resistivity-based saturation model can reduce the uncertainty of the saturation estimates. Here we document the petrophysical evaluation of the data acquired, assessing heterolithic low resistivity pay with wireline log measurements combined with core data. Focus on the coring strategy, recommendations on sampling intervals for the core analysis, and key logging measurement requirements. The results show substantial improvements in the understanding of the hydrocarbon saturation, ultimately increasing in-place volume estimates. The integrated analysis, including NMR measurements, helps to delineate the fluid contacts, further reducing the uncertainty on the recoverable net pay thickness. The core data validate the independent log-based laminated sand analysis. This illustrates how an integrated approach combining core measurements, logs, and formation testing provide an accurate evaluation of low resistivity pay reservoirs, reducing the uncertainty in the technical economical evaluation.
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