Geosteering is a sequential decision process under uncertainty. The goal of geosteering is to maximize the expected value of the well, which should be defined by an objective value-function for each operation.In this paper we present a real-time decision support system (DSS) for geosteering that aims to approximate the uncertainty in the geological interpretation with an ensemble of geomodel realizations. As the drilling operation progresses, the ensemble Kalman filter is used to sequentially update the realizations using the measurements from real-time logging while drilling. At every decision point a discrete dynamic programming algorithm computes all potential well trajectories for the entire drilling operation and the corresponding value of the well for each realization. Then, the DSS considers all immediate alternatives (continue/steer/stop) and chooses the one that gives the best predicted value across the realizations. This approach works for a variety of objectives and constraints and suggests reproducible decisions under uncertainty. Moreover, it has real-time performance.The system is tested on synthetic cases in a layer-cake geological environment where the target layer should be selected dynamically based on the prior (predrill) model and the electromagnetic observations received while drilling. The numerical closed-loop simulation experiments demonstrate the ability of the DSS to perform successful geosteering and landing of a well for different geological configurations of drilling targets. Furthermore, the DSS allows to adjust and reweight the objectives, making the DSS useful before fully-automated geosteering becomes reality.

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Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional wells, including the Arps model, stretched exponential model, Duong model, and combined capacitance-resistance model. However, it is not straightforward to determine which model should be used, as multiple models may fit a dataset equally well but provide different forecasts, and hastily selecting a model for probabilistic DCA can underestimate the uncertainty in a production forecast. Data science, machine learning, and artificial intelligence are revolutionizing the oil and gas industry by utilizing computing power more effectively and efficiently. We propose a data-driven approach in this paper to performing short term predictions for unconventional oil production. Two states of the art level models have tested: DeepAR and used Prophet time series analysis on petroleum production data. Compared with the traditional approach using decline curve models, the machine learning approach can be regarded as” model-free” (non-parametric) because the pre-determination of decline curve models is not required. The main goal of this work is to develop and apply neural networks and time series techniques to oil well data without having substantial knowledge regarding the extraction process or physical relationship between the geological and dynamic parameters. For evaluation and verification purpose, The proposed method is applied to a selected well of Midland fields from the USA. By comparing our results, we can infer that both DeepAR and Prophet analysis are useful for gaining a better understanding of the behavior of oil wells, and can mitigate over/underestimates resulting from using a single decline curve model for forecasting. In addition, the proposed approach performs well in spreading model uncertainty to uncertainty in production forecasting; that is, we end up with a forecast which outperforms the standard DCA methods.

In this paper, production characteristics of tight oil reservoirs are summarized and analyzed, the investigated reservoirs include Cardium sandstone reservoir and Pekisko limestone reservoir. The phenomenon that gas and oil or water and oil are co-produced at an early stage of exploitation has been observed. In addition, water cut of many tight oil producers remains constant or undergoes reduction as production proceeds within first 36 months. Since an oil rate drops quite a lot in the first year's production of tight oil reservoirs, reservoir simulations are run to investigate an effect of different parameters on tight oil production. Randomized experiments are created with geological and engineering parameters as uncertain factors and an oil rate as the response factor. The method of analysis of variance (ANOVA) is used to analyze the difference between group means and to determine statistical significance. Reservoir properties such as permeability, pressure, wettability, oil API, and oil saturation and engineering parameters including a fracture stage and well operations have tremendous effects on oil production. Oil recovery factor increment in tight oil reservoirs highly depends on enlarging a contact area, improving oil relative permeability, reducing oil viscosity and altering wettability. Future research and development trends in tight oil exploitation are highlighted. As primary recovery is quite low in tight oil reservoirs, the multistage fracturing technology is a necessity and it must be conducted based on a deep understanding of petrophysical and geomechanical properties. Water alternating gas (WAG) seems the best fit for tight oil exploitation. The way to improve WAG performance, including CO2 foam stabilized with surfactant or nanoparticles, low salinity water or nanofluids alternating CO2, will earn more and more attention in the future of tight oil development.

Summary Decline-curve analysis (DCA) for unconventional plays requires a model that can capture the characteristics of different flow regimes. Thus, various models have been proposed. Traditionally, in probabilistic DCA, an analyst chooses a single model that is believed to best fit the data. However, several models might fit the data almost equally well, and the one that best fits the data might not best represent the flow characteristics. Therefore, uncertainty remains regarding which is the “best” model. This work aims to integrate model uncertainty in probabilistic DCA for unconventional plays. Instead of identifying a single “best” model, we propose to regard any model as potentially good, with goodness characterized by a probability. The probability of a model being good is interpreted as a measure of the relative truthfulness of this model compared with the other models. This probability is subsequently used to weight the model forecast. Bayes' law is used to assess the model probabilities for given data. Multiple samples of the model-parameter values are obtained using maximum likelihood estimation (MLE) with Monte Carlo simulation. Thus, the unique probabilistic forecasts of each individual model are aggregated into a single probabilistic forecast, which incorporates model uncertainty along with the intrinsic uncertainty (i.e., the measurement errors) in the given data. We demonstrate and conclude that using the proposed approach can mitigate over/underestimates resulting from using a single decline-curve model for forecasting. The proposed approach performs well in propagating model uncertainty to uncertainty in production forecasting; that is, we determine a forecast that represents uncertainty given multiple possible models conditioned to the data. The field data show that no one model is the most probable to be good for all wells. The novelties of this work are that probability is used to describe the goodness of a model; a Bayesian approach is used to integrate the model uncertainty in probabilistic DCA; the approach is applied to actual field data to identify the most-probable model given the data; and we demonstrate the value of using this approach to consider multiple models in probabilistic DCA for unconventional plays.