Stylolites are rough surfaces that form by pressure solution, and present variable geometries and spatial distributions. Despite being ubiquitous in carbonate rocks and potentially influencing fluid flow, it is not yet clear how the type and distribution of stylolite networks relate to lithofacies. This study investigates Lower Cretaceous platform carbonates in the Benicàssim area (Maestrat Basin, Spain) to statistically characterize stylolite morphology and stylolite network distributions in a selection of typical shallow-marine carbonate lithofacies, from mudstones to grainstones. Bedding-parallel stylolite networks were sampled in the field to quantify stylolite spacing, wavelength, amplitude, intersection morphology and connectivity. Grain size, sorting and composition were found to be the key lithological variables responsible for the development of rough anastomosing stylolite networks. Poorlyconnected stylolites with large vertical spacings were found to be dominant in grainsupported lithofacies, where grains are fine and well sorted. Anastomosing stylolite networks appear well developed in mud-supported lithofacies with poorly-sorted clasts that are both heterogenous in size and composition. Mud-supported facies feature stylolites that are closely spaced, have high amplitudes and intersection densities, and predominantly present suture and sharp-peak type morphologies. Larger grains and poor sorting favour the formation of stylolites with small vertical spacings, low wavelengths and high amplitudes. This statistical analysis approach requires only limited information, such as that from drill core, and can be used to characterise stylolite morphology and distributions in subsurface carbonate reservoirs.
There is an ongoing debate on whether stylolites act as barriers or conduits for fluids, or even play no role in terms of fluid transport. This problem can be tackled by examining the spatial and temporal relationships between stylolites and other diagenetic products at multiple scales. Using the well-known Lower Cretaceous Benicàssim case study (Maestrat Basin, E Spain), we provide new field and petrographic observations of how bedding-parallel stylolites can influence different diagenetic processes during the geological evolution of a basin. The results reveal that stylolites can serve as baffles or inhibitors for different carbonate diagenetic reactions, and act as fronts for dolomitization, dolomite recrystallization and calcitization processes. Anastomosing stylolites, which pre-date burial dolomitization, likely acted as a collective baffle for dolomitization fluids in the study area, resulting in stratabound replacement geometries at the metre-to-kilometre scale. The dolomitization front weaves up and down following consecutive anastomosing stylolites, which are typical of mud-dominated facies that characterize limestone-dolostone transition zones. Contrarily, dolostone bodies tend to correspond to grain-dominated facies characterized by parallel (non-anastomosing) stylolites. The same stylolites subsequently acted as fluid flow conduits and barriers again when the burial and stress conditions changed. Stylolites within dolostones close to faults are found corroded and filled with saddle dolomite riming the stylolite pore, and high-temperature blocky calcite cements filling the remaining porosity. The fluids responsible for these reactions were likely released from below at high pressure, causing hydraulic brecciation, and were channelised through stylolites, which acted as fluid conduits. Stylolites are also found acting as baffles for subsequent calcitization reactions and occasionally appear filled with iron oxides released by calcitization. This example demonstrates how the same type of stylolites can act as barriers/inhibitors and/or conduits for different types of diagenetic reactions through time, and how important it is to consider their collective role when they form networks.
Summary Predicting oilfield performance is extremely challenging because of the large number of variables that can influence and control it. Traditional methods such as decline-curve analysis have been commonly used but have been shown to have significant shortcomings. In recent years, advances in machine learning (ML) have provided a new suite of tools to tackle complex multivariant problems such as understanding oil-reservoir performance and predicating the final recovery factor. In this study, the application of a random-forest algorithm to train three predictive models and investigate the influence of the various input variables was investigated. To train the algorithm, a database was built that includes information on 32 variables from 93 reservoirs from the Norwegian Continental Shelf. These variables control or potentially influence field performance and include factors that are a function of geology, subsurface conditions, fluids, and the engineering decisions taken in field development. In addition to these controlling parameters, data were also recorded for the fields that record performance. These included information on the estimated recovery factor and production rates. Eighty percent of the data were input into the random-forest algorithm to train the models, whereas 20% were retained to blind test the subsequent models. Model accuracy was measured by comparing actual and predicted observations for each prediction metric using an R2 score, mean square error, and root mean square error. The production-rate model had a mean square error of 0.004, whereas the mean square error for recovery factor was 0.024. Estimates of average monthly depletion rate have a mean square error of 0.0104. Predictor importance estimates indicate that geology/depth-dependent variables such as stratigraphic heterogeneity, reservoir depth of burial, average porosity, and diagenetic impact are among the variables with high importance in predicting recovery factor. When predicting reservoir-oil rate, the most important variables are related to field size, such as cumulative oil produced, number of wells, oil in place (OIP), and bulk rock volume. In this study, we provide data-driven insight into understanding the relationship between subsurface and engineering conditions of reservoir producibility; we also provide a tool for predicating reservoir performance within a basin or region.
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