Basin Modeling

PHIL has successfully predicted the locations of sand bodies, sequence boundaries, and source rock intervals along stratigraphic cross-sections. This is accomplished by modeling the response of the sedimentary system to the combination of local tectonics and sediment supply, and the eustatic history.

The input data required are observations in the form of digitized and age-dated horizons constructed from seismic data, well logs, or outcrops. You also need to have an estimate of paleowater depths. This can be derived from paleontologic data or seismic profiles.

This information is backstripped to the determine the sediment supply and subsidence rates at each location between each horizon. A series of model runs are executed, adjusting the parameters to minimize the difference between the observations and the model results.

PHIL can automatically invert a stratigraphic section by loading an observation dataset that consists of dated horizon depths and paleobathymetry. The program calculates the difference between the observed and modeled horizons and adjusts the subsidence rates or sediment supply to decrease the difference.

The input data required are observations in the form of digitized and age-dated horizons constructed from seismic data, well logs or outcrops.

This information is backstripped to determine an initial estimate of the sediment supply, paleowater depth, and subsidence rates at each location between each horizon. The backstripped results form the initial starting point for the inversion process. A series of model runs are executed, adjusting the parameters to minimize the difference between the observations and the model results. A statistical summary chart shows the progress of the inversion process. When the application produces results that are all similar in error, then the inversion process is finished.

Making a correlation or inferring a depositional history or lithofacies distribution from geologic data is a complicated and often empirical activity. The results are often based on many assumptions (concieved and forged in the "school" that comprises the interpreters background). The interpretation also contains many implications that can be compared with data and validated.

PHIL is designed as a general depositional model that responds to accommodation and attempts to minimize the prejudices of any one "school". The effects of tectonism, water level changes, and sediment supply can be adjusted to test the response of each control. All of the processes are independant and the means of controlling them are equally flexible. However, all processes are governed by physical rules and connected by a continuous time frame. The physical rules keep the processes within natural bounds. The time frame allows you to quantify the rates which are required by the geologic constraints. Where the rates are anomalous, you may question the age dates or horizon correlation. Where the processes fail to reconstruct the observations, you may question the horizon correlation.

The input data required are observations in the form of digitized and age-dated horizons constructed from seismic data, well logs, or outcrops. You also need to have an estimate of paleowater depths. This can be derived from paleontologic data or seismic profiles. PHIL can compare the model results with data that corresponds to any variable in the program. So porosity might be compared with the neutron log, etc.

PHIL can be used to predict the likely intervals and locations of source rock.

PHIL accounts for:

>> Organic material production variations through time

>> Oxydation of the Produced material as a function of water column depth

>> Dilution or Concentration of the organic material due to sedimentation

All Models are wrong, but some are useful. -George Box

Before model results are applied with confidence, a measurement of the range of possibilities and closeness of fit to the observations must be made. This can be performed at two levels. The first level measures the difference between the modeled and observed depths, interval thickness, paleobathymetry, porosity, etc. Comparison plots are made to illustrate the locations of the largest deviations. The second level performs a series of model runs evaluating the model results over a range of values for a single parameter. Each model changes the value of the parameter in question and converges on a best solution for that parameter set.

Once the program is within the best-fit model space defined by the observations, a series of model runs are performed. At the end of each run the probability of encountering one or more lithofacies types or physical condition (such as porosity greater than 15%) is measured by overlying a grid with user-defined thickness. In this case, the application looks for sand prone lithofacies (medium sand, coarse grained sand, interbedded sand, and silt) within a 5 meter thick interval. At the end of 100 or more runs, the table is plotted where each grid node value represents the weighted probability of encountering the state. In this case, we were evaluating the sensitivity of the distribution of sand to variations in the controls on gravity flow sedimentation. The orange regions indicate areas where sands occurred in 98% of the cases. In other words, deposition of sand in this location was insensitive to subtle variations in subsidence history, sediment supply, and the factors controlling gravity-flow sedimentation.

Weight Probability = (encounters/number of model runs) / Closeness of Fit Measure

There are several factors which influence the result. One factor is the shape of the sand bodies. Sands deposited in barrier island settings tend to be narrower and located in isolated patches. Basin-floor fan sands tend to be widely distributed but thin. Another factor is the final depth of the location. If the final location of a sand is moving up or down in the section due to differences in subsidence rates or thickness of the substrata, the probability will be distributed over a wider section of the column. A third factor is the sensitivity to variations in the factors controlling the processes. If the sand location doesn't change over the likely range of a variable, then the location of the sand body is insensitive to the factor.

These cross-sections are helpful in evaluating the probability of encountering the necessary structure, reservoir, and seal relationships required to trap hydrocarbons.

The Hydrocarbon Generation algorithms in Basim provide a chemical modeling system that allows you to define chemical species, reactions, and reaction networks for any set of reactions that can be modeled with Arrhenius equations. We have borrowed heavily from the work of Burnham and Braun (which was completed while they were at the Lawrence Livermore Labs) to define a standard set of species, their activation energies, and reaction pathways. The chemical species are defined by molecular composition. The reactions are defined by one or several Arrhenius equations with individual constants for each fraction. The reaction network can be defined as parallel or series.

The detailed framework provided by the stratigraphic simulation results provides a much improved framework in which to migrate fluids. The Hydrocarbon Migration algorithms in Basim simulate migration of oil, gas, and water as separate phases. Movement is driven by potentiometric gradients that are created by pressure buildup by groundwater flow and compaction, and tilting of beds. Primary migration is driven through excess fluids and pressure driven expulsion. Secondary migration due to viscous forces and gravity segregation.

The model employs a finite difference solution within a Lagrangian grid.

A natural extension of stratigraphic simulation is to use the constructed rock density/porosity distribution to model the pattern of seismic reflectors. Reflections was developed to allow you to produce a seismic model under various source wavelet and sampling conditions. It produces a normal incidence response by convolving the source wavelet with the impedance contrasts generated by the velocity contrasts within the section. The velocities are calculated by the methods reviewed by A. R. Gregory, 1976, AAPG Memoir 26.

Analysis of amplitude changes versus offset in seismic data is proving to be a valuable tool in locating gas reservoirs. However, in order to establish the importance of the anomalies it is necessary to calibrate to known data or make an educated estimate of rock properties in the seismic profile. Stratigraphic simulation provides a technology for predicting the size and positions of reservoirs and their associated rock properties and the presence of hydrocarbons from a limited dataset.

Basim is the most complete stratigraphic simulation and hydrocarbon generation and migration system available. This system provides a tool for dynamically modeling the distribution of lithofacies as well as hydrocarbon generation and migration history. The program allows you to simulate siliciclastic and carbonate deposition in many tectonic settings. The model builds a depth section with a description of possible rock properties that include lithology, porosity, fluid type, pressure, and temperature.

The input data includes two-way travel time horizon data from workstation interpretations, horizon ages, and depths from well logs and checkshot surveys, and estimated paleobathymetric profiles for each horizon. This information is statistically compared with the model results and automatically "inverted" by adjusting the depositional and tectonic process inputs to determine a best-fit model for the given observations.

The stratigraphic simulation can be iteratively run, fine-tuning the tectonic process and sediment supply parameters within the window of possibilities determined by the observations. The results are sampled for various conditions, such as the presence of a particular sand type or porosity greater than a predetermined value. The result produces a probability of encountering that condition.

The model calculates the rate of siliciclastic sediment influx over time. This information can be used to estimate channel width and depth, as well as document important shifts in deposition in and out of the play fairway.

A distribution of Vp and Vs can be calculated from the stratigraphic model using the White-Boltzman equation or similar method and values of the bulk modulus of the matrix and framework material (which vary as a function of lithology, age, depth, and porosity), and pore filling fluid. Porosity is modeled as a function of depth or overburden stress. Density is calculated as a function of the mineral type and porosity. An impedance/reflectivity model can be determined from the distribution of velocities. The fluid content, as calculated by the fluid-flow model, can also determine the velocity of the rock.

In addition to calibrating the velocity state of a seismic profile, the simulator may also suggest new play types or positions of important unconformities and associated hydrocarbon bearing reservoirs.

PHIL and Basim provide depth conversion facilities. Horizons imported from Landmark or Geoquest workstations, or our own format can be converted from two-way travel time to depth in meters according to a set of checkshot survey functions. The checkshot survey functions are adjusted according to local water depths. The results can be exported to other programs or used in the stratigraphic simulation process.

Within PHIL and Basim is a two-dimensional analysis that incorporates the effects of water level and deposition.

The input data required are observations in the form of digitized and age-dated horizons constructed from seismic data, well logs, or outcrops. You also need to have an estimate of paleowater depths. This can be derived from paleontologic data or seismic profiles.

This information is backstripped to the determine the sediment supply and subsidence rates at each location between each horizon.

Basinworks performs a one-dimensional analysis that incorporates the effects of water level, deposition, and erosion.

The input data required are observations in the form of digitized and age-dated horizons, lithology, and the amount of erosion constructed from well logs or an outcrop section. You also need to have an estimate of paleowater depths. This can be derived from paleontologic data or seismic profiles.

This information is backstripped to determine the sediment accumulation rates, geohistory, and subsidence rates at the location between each horizon.