Abstract:
The present disclosure describes a method that improves the long-range geobody continuity in Multiple Point Statistical methods, wherein the coarsest multi-grid level cells are simulated in a regular path, and the subsequent level cells are simulated in a random path as usual. The method is general and is applicable to different cases: such as hard data conditioning, soft data conditioning, non-stationarity modeling, 2 or more than 2 types of facies modeling, and 2D and 3D modeling. The method is particularly useful in reservoir modeling, especially for the channelized systems, but can be generally applied to other geological environments.
Abstract:
A method of reducing gas flaring through modelling of reservoir behavior using a method of optimizing oil production from one or more well(s) in a reservoir, the method providing a model of the well, inputting well data for a one or more well(s) into the model, the well data selected from geological layers, reservoir properties, fracturing data, completion data, permeability data, geochemistry, and combinations thereof. Inputting historical production data from one or more well(s) into the model, the historical data selected from PVT data, BHP, oil production rates, gas production rates and water production rates, or combinations thereof. Controlling the model to match one or more parameters selected from production rates, gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or a combination thereof in a probabilistic manner to obtain a plurality of historical models. Verifying one or more test models against the historical models to identify an optimal model with minimum error. Using the optimal model to predict one or more parameters selected from production rates, gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or a combination thereof from the well into a future. Optimizing a production plan using the predicted parameters and implementing the optimized production plan in said well, whereby oil production is optimized as compared to a similar well produced without the optimized production plan.
Abstract:
A method for modeling a reservoir is described. One example of a method for modeling a 3D reservoir involves using multiple-point simulations with 2D training images.
Abstract:
Estimating in-situ stress of an interval having drilling response data is described. Estimating involves obtaining drilling response data of a data rich interval with available data. Estimating relative rock strength as a composite value that includes in-situ stress and rock strength. Estimating a Poisson's ratio from the relative rock strength. Generating a stress model that includes uniaxial strain model using the Poisson's ratio. Verifying the stress model with the available data. Applying the stress models in a non-data rich interval.
Abstract:
A method of computer modeling a reservoir using multiple-point statistics from non-stationary training images is provided. Some methods include: a) identifying a path via a computer processing machine to visit all nodes of a simulation field; b) setting a template for searching data event in the simulation field and for searching data event replicates in the non-stationary training image; c) defining a neighborhood in which the training image is sampled; d) formulating a kernel function that gσ(d) that decreases from 1 to 0 when distance d increases from 0 to infinity; e) for the current node in the simulation filed, identifying the data event covered by the template; f) randomly sampling the training image in the neighborhood of corresponding node in the training image until an exact or approximate replicate of the data event is found; g) computing distance d between central node of the replicate and simulation node; h) computing the kernel function; i) drawing a random number u between 0 and 1; j) assigning value of central node of the replicate to the simulation node if gσ(d) is greater than u; k) repeating steps f) to j) if gσ(d) is not greater than u; and repeating steps e) to k) until all simulation nodes are visited