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The shales of Sekuliye Formation were investigated to interpret their provenance history, weathering, and tectonic setting based on major and trace element geochemistry. The major and trace element geochemistry revealed derivation of the shales from felsic igneous rocks with little influence from intermediate igneous rocks and quartzose sedimentary rocks. Weathering proxies such as plagioclase index of alteration (PIA), chemical index of alteration (CIA), index of chemical variability (ICV) and Rb/Sr ratios show that the source rock undergoes high degree of chemical weathering activities. Also, the plot CIA vs. ICV shows that all the studied shales were plotted below Post Archean Australian Shale. The ICV value of 0.85 also indicates high intensity of weathering activities in the provenance area. The paleoclimatic indicators (C values and Sr/Cu) and some trace elements ratios (Ni/Co and Cu/Zn) displayed deposition under humid conditions and oxic depositional environment, respectively. The Sr/Ba ratios revealed high salinity during the deposition of the studied shale, reflecting a marine depositional environment. Through geochemical studies, these sediments were classified as shales that are texturally immature and compositionally matured. The geochemical data plotted on tectonic setting discriminant function diagrams suggested a continental rift of passive margin settings. Accordingly, the inferred tectonic settings are comparable to the accepted model on the origin and evolution of the Benue Trough.

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.

The methods used to estimate recovery factor change through the life cycle of a field. During appraisal, prior to development when there are no production data, we typically rely on analog fields and empirical methods. Given the absence of a perfect analog, these methods are typically associated with a wide range of uncertainty. During plateau, recovery factors are typically associated with simulation and dynamic modeling, whereas in later field life, once the field drops off the plateau, a decline curve analysis is also used. The use of different methods during different stages of the field life leads to uncertainty and potential inconsistencies in recovery estimates. A wide range of interacting, partially related, reservoir and production variables controls the production and recovery factor. Machine learning allows more complex multivariate analysis that can be used to investigate the roles of these variables using a training data set and then to ultimately predict future performance in fields. To investigate this approach, we used a data set consisting of producing reservoirs all of which are at plateau or in decline to train a series of machine-learning algorithms that can potentially predict the recovery factor with minimal percentage error. The database for this study consists of categorical and numerical properties for 93 reservoirs from the Norwegian Continental Shelf. Of these, 75 are from the Norwegian Sea, the Norwegian North Sea, and the Barents Sea, whereas the remaining 18 reservoirs are from the Viking Graben in the UK sector of the North Sea. The data set was divided into training and testing sets: The training set comprised approximately 80% of the total data, and the remaining 20% was the testing set. Linear regression models and a support vector machine (SVM) models were trained with all parameters in the data set (30 parameters); then with the 16 most influential parameters in the data set, the performance of these models was compared from results of fivefold crossvalidation. SVM training using a combination of 16 geologic/engineering parameters models with Gaussian kernel function has a root-mean-square error of 0.12, mean square error of 0.01, and [Formula: see text]-squared of 0.76. This model was tested on 18 reservoirs from the testing set; the test results are very similar to crossvalidation results during models training phase, suggesting that this method can potentially be used to predict the future recovery factor.

A number of geological and engineering parameters influence and control the performance and ultimate recovery from an oil reservoir. These are commonly interlinked and the relative importance of each can be difficult to unravel. These variables include geological parameters such as depositional environment, which has long been considered to be a key factor influencing the production characteristics of fields. However, quantifying the importance of any single factor, such as depositional environment, is complicated by the impact of the other variables (geological and engineering) and their numerous interdependencies. The main aim of this study is to unravel the impact of the depositional environment and primary facies architecture on reservoir performance using an empirical study of oilfields from the Norwegian Continental Shelf. A database of 91 fields, with a total of 7.8 Bbbl (billion barrels) of oil in place, has been built. Within this a total of 93 clastic reservoirs were classified into three gross depositional environments: continental, paralic/shallow marine and deep marine. The reservoirs were further classified into eight depositional environments in order to provide further granularity and to capture their depositional complexities. A further 28 parameters which capture other aspects that also impact production behaviour, such as reservoir depth, fluid type and structural complexity, were recorded for each reservoir. Principal component analysis (PCA) was utilized to explore the importance of sedimentological-dependent variables in the dataset, and to determine the parameters that have the strongest influence on the overall variability of the dataset. PCA revealed that parameters associated with field size and depth of burial had the most influence on recovery factor. Gross depositional environment and other stratigraphic-dependent parameters were the most significant geological factors. Fluid properties, such as API gravity and average gas/oil ratio, were unexpectedly among the less important parameters.

The upper Bima Formation in the Northern Benue Trough has been interpreted as a distributive fluvial system. Previous stratigraphic studies on the Bima Formation were focussed on localized, qualitative, sedimentary facies analysis without regional context or quantitative information about sand-body scale and architecture. This study quantitatively analysed sand-body thickness variations in the upper member of the Bima Formation across the Northern Benue Trough, documented the spatial variations in channel dimensions. Photo-realistic virtual outcrops were generated for four study sites using data acquired with an unmanned aerial vehicle and processed photogrammetrically. Analysis of the virtual outcrops illustrated spatial variation in the thickness of channel infill components, a downstream decrease in proportion of multi-storey channel belt facies association (from 98% to 5%), an increase in floodplain facies association (from 0% to 13%), an increase in the proportion of isolated channel fill facies association (from 2% to 80%) and a slight decrease in average grainsizes of channel fill sandstone . These observations are interpreted to represent evidence of a distributive fluvial system (DFS) or mega-fan with the proximal part of system at Tula, Ture and Tashan Alaji to the medial part at Hinna having an increase in frequency and thickness of the flood plain facies association and abundance of isolated channel fill facies associations. This study demonstrates the regional facies variability of the upper Bima Formation and has provided a basis for comparison with other ancient distributive fluvial system.