Saudi Arabian Oil Company

Marei Saeed Al-Garni

Mohammed Sabri Al Madani

Otto E Meza Camargo

Otto Meza Camargo

Mohamed Bouaouaja

Mustafa Amari

Maan Abdullah AlHawi

Maan Abdullah Alhawi

Abdulrahman Albar

Anwar Rahim Awan

Mohammed AlRashed

Permeability in earth models has three components: natural fracture, distinctive matrix permeability and porosity correlated matrix permeability. While matrix permeability is usually predictable from porosity and diagenesis effects, fracture permeability can be a highly uncertain parameter. The earth model permeability components are calibrated by determining fracture distribution and estimating fracture properties through an iterative optimization process. The calibration proceeds iteratively until the current estimate of calculated reservoir flow capacity is within acceptable accuracy limits to reservoir flow capacity indicated from production logging tool measurements.

Modeling reservoir permeability through estimating natural fracture distribution and properties

A method for variogram modeling is disclosed. The method includes obtaining a synthetic well data and a well data for facies or petrophysical properties of interest in a targeted reservoir zone, training machine learning models using the synthetic well data as inputs and outputting a plurality of final variogram parameters predicted by using a plurality of machine learning models for the facies or petrophysical properties of interest in the targeted reservoir zone, wherein the well data for the facies or petrophysical properties of interest in the targeted reservoir zone is used as input.

Fast variogram modeling driven by artificial intelligence

A method may include obtaining, by a computer processor, seismic data regarding a geological region of interest. The method may further include obtaining, by the computer processor, well log data from a wellbore within the geological region of interest. The method may further include determining, by the computer processor, a formation top depth using the seismic data, the well log data, a stratigraphic column, and a machine-learning model. The stratigraphic column may describe an order of various formations within the geological region of interest. The machine-learning model may assign a feature among the seismic data and the well log data to a formation among the formations in the stratigraphic column to determine the formation top depth.

Method and system for predicting formation top depths

A methodology is provided to optimize the dynamic connectivity of a discrete fracture network (DFN) model of a subsurface reservoir against observed reservoir production measures using streamlines and machine learning. Adjustment of discrete fracture network properties of the reservoir is made locally and minimizes computer processing time spent in history matching. An iterative workflow identifies history match issues between measured and predicted or simulated water cut of reservoir produced fluids. Streamline analysis quantifies injector-producer communication and identifies reservoir grid block bundles that dominate dynamic response. A genetic algorithm updates discrete fracture network properties of the reservoir model to improve dynamic history match response.

Optimization of discrete fracture network (DFN) using streamlines and machine learning

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Dhahran, Saudi Arabia

Marei Saeed Al-Garni