|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Abstract History matching is a critical part of the simulation workflow. History matching in shale is unique because it involves data from both hydraulic fracturing and from production. This paper presents a step by step workflow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. We perform sensitivity analysis simulations using a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop ‘motifs’ that inform the history matching process. Using intuition from these simulations, we show how to expedite history matching by changing matrix permeability, fracture conductivity, matrix pressure dependent permeability, boundary effects, and relative permeability. Finally, a worked example, using two Eagle Ford hydraulically fractured wells, is presented to demonstrate the workflow.
Abstract The fracturing of horizontal wells is a recently developed tool to help enable tight and shale formations to produce economically. Production data analysis of the wells in such formations is frequently performed using analytical and semi-analytical methods. However, in the presence of nonlinearities such as multi-phase flow and geomechanical effects, the numerical simulations are necessary for interpretations and history-matching techniques as they are required for model calibration. Reservoir history-matching techniques are usually based on the frequentist approach and can provide a single solution that can maximize the Likelihood function. Production forecasts using a single calibrated model cannot honor the uncertainty in the model parameters. Therefore, a Bayesian approach is suggested where we can combine our prior knowledge about the model parameters together with the Likelihood to update our knowledge in light of the data. The Bayesian approach is enriched by applying a Markov chain Monte Carlo process to updated the prior knowledge and approximate the posterior distributions. In this paper, a one-year production data of a real gas condensate well in a Canadian tight formation (lower Montney Formation) is considered. This is a horizontal well with eight fracture stages. A representative 2D model is constructed which is characterized by 17 parameters which include relative permeability curves, capillary pressure, geomechanical effects, fracture half-length, fracture conductivity, and permeability and water saturation in the stimulated region and the matrix. Careful analysis of available data provide acceptable prior ranges for the model parameters using non-informative uniform distributions. Markov chain Monte Carlo algorithm is implemented using a Gibbs sampler and the posterior distributions are found. The results provide an acceptable set of models that can represent the production history data. Using these distributions, a probabilistic forecast is performed and P10, P50 and P90 are estimated. This paper highlights the limitations of the current history-matching approaches and provides a novel workflow on how to quantify the uncertainty for the shale and tight formations using numerical simulations to provide reliable probabilistic forecasts.
Abstract Gas flow in shale gas reservoirs occurs primarily from ultra low permeability shale rocks through a complex network of natural and induced hydraulic fractures. Consequently, fracture parameters (conductivity and half length), fracture location and distribution are the dominant factors influencing well drainage volumes and shale gas well performance. Stimulated reservoir volume or SRV, estimated from microseismic event clouds or rate/pressure transient analysis, describes a measurement of overall reservoir volume impacted by fracture treatments. With SRV as well as the dynamic production/pressure response, reservoir simulation models can be calibrated to actual well performance in shale gas reservoirs leading to improved understanding, forecasting and future well placement. In this paper, we first introduce a novel approach for computing well drainage volume for shale gas wells with multistage fractures and fracture clusters. Next, we calibrate the shale gas reservoir model by matching the drainage volume with the SRV within specified confidence limits. The matching of the SRV is done in addition to the traditional history matching of production/pressure response and further constrains the estimation of fracture parameters. An evolutionary algorithm with design of experiments is used for the assisted history matching. Sensitivities to various parameters such as fracture conductivity, fracture half lengths and rock compaction have also been investigated. The proposed approach has been applied to a generic shale gas well designed after a real field case. The results clearly indicate the benefits of including SRV during history matching, leading to improved fracture/matrix parameter estimation and performance forecasting. Our proposed approach provides an important tool that can be used to optimize well placement, fracture treatments and improve the economics of shale gas plays.
Chen, Chaohui (Shell International Exploration & Production Inc.) | Li, Ruijian (Shell Exploration & Production Co.) | Gao, Guohua (Shell Global Solutions (US) Inc.) | Vink, Jeroen C. (Shell Global Solutions International B.V.) | Cao, Richard (Shell Exploration & Production Co.)
Summary For unconventional reservoirs, it is very difficult to determine the values of key parameters or properties that govern fluid flow in the subsurface due to unknown fracture growth and rock properties. These parameters generally have quite large uncertainty ranges and need to be calibrated by available production data. Using an ensemble of history matched reservoir models to predict the Estimated Ultimate Recovery (EUR) is one of popular approaches when parallelized computing facilities become cheaper and cheaper to customers. The Randomized Maximum Likelihood (RML) method has been proved quite robust for generating multiple realizations by conditioning to production data. However, it is still expensive to apply traditional optimization algorithms to find a conditional realization by minimizing the objective function defined within a Bayesian framework, especially when adjoint-derivatives are unavailable. How to generate multiple conditional realizations efficiently is critically important but still a very challenging task for proper uncertainty quantification. In this paper, a novel approach that hybrids the direct-pattern-search and the Gauss-Newton algorithm is developed to generate multiple conditional realizations simultaneously. The proposed method is applied to history match a real unconventional Liquid Rich Shale reservoir. The reservoir is stimulated by multiple stage hydraulic fractures. In this example, uncertainty parameters include those characterizing uncertainties of reservoir properties (including matrix permeability, permeability reduction coefficient, porosity, initial water saturation and pressure) and those for hydraulic fractures (height, width, length, and effective permeability of SRV zone). Uncertainty of production forecasts are quantified with both unconditional and conditional realizations. The case study indicates that the new method is very efficient and robust. Uncertainty ranges of parameters and production forecasts before and after conditioning to production data are quantified and compared. The new approach enhances the EUR assessment confidence level and therefore significantly reduces risks for unconventional assets development. Introduction For unconventional reservoirs, the key reservoir properties, such as effective flowing fracture length (Xf), effective fracture height (Hf), permeability and permeability reduction coefficient, fracture conductivity (FCD), drainage area (A) etc., that govern fluid flow in subsurface are very difficult to obtain due to unknown fracture growth in tight rock. Uncertainties associated with these parameters are usually quite large. Understanding the uncertainty of the subsurface model is helpful to define how to drill wells and determine fracture stages spacing or the number of wells. There are mainly three categories of Estimated Ultimate Recovery (EUR) prediction methodologies for unconventionals:
Eltahan, Esmail (The University of Texas at Austin) | Yu, Wei (The University of Texas at Austin) | Sepehrnoori, Kamy (The University of Texas at Austin) | Kerr, Erich (EP Energy) | Miao, Jijun (SimTech LLC) | Ambrose, Ray (EP Energy)
Abstract Unconventional reservoirs typically exhibit large uncertainty in rock and fracture properties coupled with significant heterogeneity making manual history matching a challenging endeavor. Computer-assisted methods of history matching gained preference because they honor the non-uniqueness of the possible geological and fracture realizations by allowing efficient simulation sensitivities over many possible outcomes. This study integrates EDFM's fracture-modeling capabilities into an artificial intelligence-based history matching and optimization tool, to achieve more confidence and capability in simulations for production forecasting and reservoir-behavior studies. This approach involves four main steps: (1) initializing EDFM-fracture parameters for a realization, (2) running a pre-simulation command that calls the EDFM-preprocessing engine and generates an updated model, (3) running the model on an appropriate simulator, and (4) calculating the objective function. We apply this method to perform history matching for a horizontal tight-oil well in the middle Bakken. We further repeat history matching for the same model after including a set of 1,000 randomly distributed natural fractures. In both studies, four embedded-fracture parameters are set to vary within predefined continuous ranges. The objective is to minimize history matching error for field-recorded, bottom-hole pressure and gas production data with oil production history set as a constraint. This new method, with a more realistic fracture geometry, resulted in a better match to the field-recorded data compared to a previous study that was based on a local gird refinement model. After introducing natural fractures to the model, history-matching results displayed a trend of decreasing rock-matrix permeability and fracture conductivity compared to the original scenario without natural fractures. In this case, history matching error response was most sensitive to rock-matrix permeability followed by fracture half-length. We then made 30-year production forecast using the most optimal solutions. Accepted solutions were screened based on a cut-off value of 5% normalized history-matching error. We finally demonstrate that, even though the presence of natural fractures significantly altered the model, it did not have a major influence on long-term production.