Real-Time 3D Earth Model Updating While Drilling a Horizontal Well

Abstract

We describe here a test of a new technology for successful drilling of horizontal wells in thin oil columns. We constructed a three-dimensional (3D) earth model of the overburden and of the target reservoir layers on the basis of predrilling data and updated this model in real time on the basis of logging-while-drilling (LWD) measurements transferred to remote locations using the World Wide Web. This strategy allowed us to check and update the planned drilling trajectory continuously with all the information available at any given time. We used uncertainties in the depth of markers observed in a number of offset vertical wells to determine the uncertainty in the thickness of layers in the earth model. This 3D model comprised best estimates of the thicknesses and a covariance matrix that quantified their initial uncertainties. We then drilled a pilot well. Trajectory data, LWD logs, and resistivity images from downhole measurement-while-drilling (MWD) and LWD tools were transmitted in real time from the Simpson No. 22 drill site in Indiana to a prototype application running in Connecticut. As we acquired new measurements in the pilot well, we compared log curves predicted by the model to the measured logs. Our prototype allowed an interpreter to update the location of markers as the well was drilled; an update of the entire 3D earth model and its uncertainty was then automatically computed in near-real time. Quantified uncertainties are key in this stage to ensure that the model update is in agreement with all the data considered previously. This procedure was repeated while drilling the horizontal drain hole, which was successfully steered within a dipping 6-ft-thick layer for 808 ft. Our prototype also allowed for remote collaboration: 3D model updates, LWD data, and resistivity images were available to collaborators who were connected to the network and simultaneously ran copies of the prototype at additional locations. In particular, the remote availability of real-time resistivity images was key to the successful well placement, as these images show how the well trajectory follows the layering. Remote collaboration means that drilling decisions can be made collaboratively by a globally distributed team in a secure network environment. This can be a key capability for geosteering, especially in remote locations or when staffing is constrained. Introduction High-angle and horizontal wells offer the opportunity to tap previously unrecoverable hydrocarbons that occur in thin columns. To achieve this objective it is often necessary to precisely position the well with respect to fluid contacts and/or sedimentary layers. Frequently there is uncertainty in the disposition of these features, so that the target cannot be geometrically defined and real-time data on the position of the features are required to geosteer the well.1 We wished to test the application of novel completion technologies to drain oil from a very thin (originally 13-ft-thick) oil column in the Mount Vernon Unit of the Lamott Consolidated field, Posey County, Indiana (Fig. 1). To do this, we needed to drill an 808-ft-long horizontal well in oil-bearing sandstone.2 The East Mount Vernon Unit is operated by Team Energy and produces oil from the Tar Springs and Cypress sandstones. Most production is from the Mississippian Cypress sandstone reservoir. The previously existing vertical wells produce at a very high water cut (~ 95%) because of the thin nature of the Cypress reservoir oil-column. The Simpson No. 22 well was drilled first as a deviated pilot well, to penetrate the Cypress sandstone close to the planned heel of the horizontal section, and subsequently as a smooth build section (< 4°/100 ft) leading to a horizontal section in the Cypress reservoir (Fig. 2).

Assessing Borehole-Position Uncertainty From Real-Time Measurements in an Earth Model

“…In 2002 we reported on our software prototype, which uses horizon-depth markers to update the wellbore-and earth-model uncertainties in real time. 5 Here we extend this work to account for measurements of the apparent dip and azimuth at horizons and of stratigraphic dips within a layer. These markers are defined in a later section.…”

“…This work is described in our previous report. 5 Here we extend this work to include stratigraphic markers obtained from image logs.…”

“…As in our previous report, 5 we use a Bayesian approach to quantify model uncertainty from a set of measurements. This method starts with a prior probability distribution that describes our state of knowledge about model uncertainty before we account for the measurements.…”

“…Details on updating of m and m with measurements are presented in our previous report 5 . It is shown that a Bayesian update requires a data vector d, a data predictor function g( ) that makes a prediction of d based on the current model vector , and a Jacobian matrix J that is defined by …”

Optimization of Well Trajectory Under Uncertainty for Proactive Geosteering

“…To reduce risk, it is beneficial to account for the uncertainty when planning the well path. Robust optimization or optimization under uncertainty aims at finding a solution that remains close to optimal for different possible scenarios (Mulvey et al 1995;Srinivasan et al 2003;Sahinidis 2004). We take the simplest form of robust optimization, which is to minimize the expectation of a cost function for a scenario that is uncertain.…”