Reservoir modeling has become an indispensable tool in the oil and gas industry to measure risk associated with alternative production scenarios. It is no secret that nowadays more and more operators use deterministic approach for history matching but even so, the task remains technically challenging. Given a large number of uncertainties associated with subsurface models, it has become critical to produce a set of history-matched models and use them for risk assessment in predicting the future performance of the reservoir. Quantification of uncertainties is a vital process in complex fields to ensure a proper field development plan (FDP) is in place. In this paper, we present a comprehensive workflow that incorporates all processes from building static and dynamic models to producing ensemble of history matched models. To assess the mismatch with historical production data, a number of geological realizations were created and simulated using a variety of uncertainty parameters. Based on the obtained mismatch value, the posterior probability distribution for uncertain parameters updated. Different optimization methods are used to find candidates with improved match quality, and this loop is repeated until a set of diverse history-matched models are generated. These calibrated reservoir models are then used to estimate prediction uncertainties for further FDP optimization. This study is focused on automating workflows and generating multiple geological realizations that produce a set of history-matched models to probabilistically estimate prediction uncertainties and optimize FDP. We also show how these models are critical in the process of decision making and risk evaluation.

No abstract

No abstract

The current work is intended to show how Central Asian region adopted the industry's latest developments in hydraulic fracturing design optimization. The suggested workflow includes advanced proppant and acid transport modeling combined with production forecasting via connection of comprehensive hydraulic fracturing and reservoir simulators. The field examples from Kazakhstan are given to support the strengths of the suggested workflow. The suggested workflow significantly differs from the standard propped fracturing approach used in Kazakhstan. The new workflow uses one software platform for hydraulic fracturing simulations and hydrodynamic modeling. First, the comprehensive hydraulic fracturing simulator with advanced proppant and acid transport model, chemical and physical processes is used to perform a first approximation of the hydraulic fracturing design. Second, the designed fractures are incorporated into the dynamic simulator for a thorough sensitivity, uncertainty and probability analysis. Finally, the model is validated against historical production and used for forecasting and hydraulic fracturing treatment optimization.

This study suggests a robust multiple realization approach to determine optimum production stimulation strategy. Unlike the majority of existing similar studies, the suggested approach concentrates both on geological uncertainties and improving of hydraulic fracturing modeling. Existing similar studies either concentrate on the reservoir uncertainties (i.e., ignoring the importance of hydraulic fracturing simulation and calibration), or vice versa, concentrate on the sensitivity of the hydraulic fracturing parameters (i.e., ignoring the wide range of geological uncertainties). The key feature of the suggested approach is the use of a single platform for static geological model, hydraulic fracturing simulator, and hydrodynamic modeling. The study shows the example of suggested approach implementation on one of the complex oilfields in Western Kazakhstan, where approach helped to determine the probability of stimulation success and allowed to determine the optimum stimulation strategy.