The GOM3 Analytics Addon is a collection of tools to bring high end analytics and visualization to different groups working in the Gulf of Mexico. Building on top of the rich database of GOM3, and the experience of continuously expanding the features therein, the Analytics Addon is designed to streamline workflows at a time when everyone is trying to maximize efficiency.
This package includes analyses for a variety of users, from forecasts of lease sale bidding, to pipeline routing using seafloor bathymetry and hazards, to visualization of well logs both online and in 3D GIS scenes. Below are descriptions for the features of the Analytics Addon, but we are always available to show these in a live demo (the best way to view the dynamic tools).
Viewing digital well logs within a web page is now part of the Analytics Addon, as is the ability to integrate these data within a 3D GIS scene. Based on the roughly 46,000 digital well logs obtained for over 5,000 wells (most wells drilled since 2004), a “golden set” of standard log curves for each well was derived out of the nearly half million curves from these logs. There are three grouped displays: geology & geophysical logs, mud logs and mechanical logs. Further context has been added to the last track, displaying paleo observations, geomarkers, hydrocarbon bearing zones and completion intervals gathered and integrated from GOM3.
The mud log of the No. 2 BP1 well in KC292.
The tracks are dynamic, with the ability to zoom and pan and values of each curve are available when moving the cursor over the curves. Jumping to paleo or completion reports are available when clicking those features in the context part of the display.
The G&G log of the SS1 well in MC257, zoomed in on the S01 completion interval.
The other major innovation is the integration of these data within the 3D scenes of GOM3. Each of the major curves can be displayed in 25-foot increments, providing important correlations between wells, for example high resistivities, low gammas or spikes in C1. Clicking on the curve values provides a jump back to the online viewer or other well data (reports, production graphs, downloads, etc.). Combining the well logs with the context of other data in 3D dimensions increases the power of the sharper visualization that ArcGIS Pro provides.
Gamma ray values along boreholes in Mars/Ursa with an interpolated surface of the Catinaster mexicanus horizon.
Resistivity values labeled along boreholes in the N_O sand of Mars/Ursa.
Block/Bidding Evaluation Models
Earth Science Associates developed two models for forecasts of bids in future sales. The first estimates the probability of at least one bid on deep water blocks. Block characteristics, from water depth to proximity of relinquished leases, to the history of past bids, have all been tabulated and are updated daily as changes occur. Additionally, there are global variables (e.g., oil price) and company-specific variables (e.g., how far is the nearest lease owned by ENI?). In all, we evaluated two dozen variables.
For the 20 companies that dominate deep water, we calculate the marginal positive and negative preferences toward these block characteristics, as seen below.
The tornado plot identifies the explanatory variables (listed along the y-axis) shown to be statistically significant in estimation of bid likelihood for Shell in the spring 2018 sale. A positive estimate results in an increase of probability of bidding and a negative estimate decreases the probability of bidding.
Some companies are less likely to bid in deeper waters, others systematically stay close to their existing leases. The company-level analyses of each block are then combined to esti-mate the probability that each block will receive at least one bid. We call this the Bid/No-Bid model. It is estimated using logistic regressions.
A map of estimated probability of at least one bid in the Spring 2018 sale, focused on northern Green Canyon. Current Leases are shown in grey, ESA’s pre-sale probabilities of bids are shown in the legend colors and those blocks actually bid are surrounded by thick, black outlines.
The second model, shown below, forecasts the amount of the high bid in deep water blocks. Like the Bid/No-Bid model, empirical block characteristics and companies’ historically evidenced preferences are included in the Bid Amount model. This model is estimated with a multivariate linear regression of past bids.
These regression models train over specific periods, which the user controls. Any period since 2009 of at least three years can be used to train the models to forecast for 2013 through the upcoming sale. For the historical sales (2013 and spring 2018 sale), forecasts can be made to compare the forecasted probability of Bid and Bid Amount with what actually occurred in the sale.
A map of the forecasted high bid amount in the spring 2018 sale, focused on northern Green Canyon. ESA’s estimated bid amounts are shown in the legend colors.
These two models provide systematic analysis of bidding behavior and an objective set of updated benchmarks, on constantly updated information, to analyze your competitors and examine the relative attractiveness of regions and blocks within them. Through the power of GOM3, model results can be combined with the data and tools there and, more importantly, with your company’s proprietary geoscience, engineering and scouting information – as well as a company strategy.
Why ponder a big, static war-room wall map when you can use state-of-the-art models based on data refreshed daily?
With increasing water depths and the declining sizes of new finds, transportation of oil and gas from your new discovery to shore will be central to the economic viability of the project – and the amount your company is willing to bid on the block. Using a network of 78 million nodes, reflecting bathymetry and the location and types of seabed hazards, our Tie-Back Tool computes an optimal pipeline path from your discovery to as many alternative destinations as you want to test.
In a simple interface, identify the tie-back origin. We will present you with a list of the 15 nearest platforms within 75 miles, their straight-line distance, along with operator, water depth information and our estimates of the spare productive capacity (updated monthly). Set some parameters on the relative importance of route slope versus distance and maximum slope and run. Within a few minutes, you’ll receive a zip file with a proposed pipeline route, given bathymetry and hazards. Alternatively, route your tie-back directly to join an existing pipeline that may be closer than the nearest platform.
The map shows 4 tie-back routes from Alaminos Canyon 79 to 3 platforms: Nansen Spar, Hoover spar, and Gunnison spar. There are 2 routes to the Nansen spar: one avoids seafloor obstacles (magenta) and the other does not (peach).
To test sensitivities to different destinations just rerun the tool. The results include detailed summary reports going over basic statistics and characteristics of the optimal pipeline path. Like the Bid Models, the output of the Tie-Back tool also includes all of the GIS-ready results for integrating in GOM3, where it can be combined with other data like the vast sets of proprietary data needed to make the best decisions.
Graph shows the vertical profile of the difficult pipeline path from Alaminos Canyon 79 to the Hoover Spar.
The first phase of the Drilling Analysis portion of the Analytics Addon generates drilling speed and mud weight graphs of all the wells in a given area. These graphs gather all of the weekly drilling reports, permits and paleo information to display drilling depths, mud weights, fracture gradients, casing programs and paleo horizons encountered. Wells are selected by either a radius around a location or by selecting the number of closest wells.
The Depth vs Days Drilled graph of the No. 1 well in WR677. This graph, in TVD, shows paleo horizons and the casing strings.
Both Depth vs Days Drilled and Depth vs Mud Weight graphs can be shown in MD or TVD. Paleo horizons are displayed by Series, Subseries or Stage. Casing strings and proposed casing strings are displayed on the right side of each graph. Bypasses and sidetracks are indicated and maps are included to locate the wells with included data.
The Depth vs Mud Weight graph of the No. 1 well in MC726. This graph, in TVD, shows the mud weights and fracture gradients (with lines connecting the samples). Expected mud weights and fracture gradients from permit data are optional.
Forgotten Oil & Gas Study
The Forgotten Oil & Gas Study, originally created in 2014 but updated in 2018, identifies reservoirs, sands, fields and completions that appear to have unproduced hydrocarbons. The largest part and most detailed work was done on reservoirs, cleaning up the operators' names and identifying the completions within each reservoir. Once the names were matched to ensure that production from subsequent completions within a reservoir was not an issue, decline curve analysis was run on every reservoir in the Gulf of Mexico, using a statistical fit score (R2), to determine the best of the three standard models (Exponential, Cumulative, Hyperbolic). Output for all three models is downloaded as part of the output, along with other statistics of the decline curve analysis for each reservoir.
The distribution of oil and gas remaining in produced reservoirs, based on decline curve analysis throughout the Gulf.
Many reservoirs were found that were not in terminal decline at the time production ceased, so decline curve analysis could not be performed. These reservoirs were identified and high-graded by daily rate of production.
Each year, the BOEM releases the amount of cumulative production and estimated remaining reserves for each field in the Gulf. Similar statistics are released for sands within each field as well. Naturally, as new reservoirs are discovered or new technology is employed, the sum of the production and reserves (the estimated ultimate recovery) can rise. However, for unknown reasons the government will occasionally revise the estimates downward, sometimes significantly. Although there may be good reasons for doing so, these fields and sands are highlighted in this study to make the search for interesting targets more efficient.
Oil and gas production, reserves and 'de-booked' reserves for all fields and sands in the Gulf of Mexico.
The final category of data in the study gathers completion interval which had high test rates, but didn't ultimately come online. Again, there are likely a variety of reasons for this to happen, but this study can save time identifying potential targets that are worthy of more investigation.
Overall, the Forgotten Oil & Gas Study is the perfect tool for quickly identifying targets with known hydrocarbons (lowering risk) and saving time for those who wish to work up old fields and save money doing so.
To complement the forecast models, we have prepared a set of pre-made charts and maps that distill key decisions made in all previous deep water sales (1983 to present). They bring company statistics into focus: bidding budgets, efficiency of those expenditures (amounts exposed in winning and losing bids, money left on table) how those parameters have changed over time. Other charts summarize what happened in a given sale or in all sales over a specific period.
Chart shows the bid amount of Chevron from 2009 through 2017. The top chart shows a box and whisker plot of the bid amount distribution for each sale and the bottom chart shows a bar graph identifying money spent on winning bids (money left on table and money required to win) and losing bids.
We have also introduced a network analysis chart that is new to the industry. Each company bidding in a sale is a node; blocks bid in the sale are also nodes. Links from company nodes join to the nodes they bid on (gold for winners, grey for losers) When companies bid jointly, the company links meet at an intermediate node and then connect to blocks bid by that partnership. When multiple bidders connect to the same block, the block node turns to a pie diagram, showing the amount required to win the block (2nd highest bid + $1) and difference between that amount and the high bid – what was left on the table.
Since 2009, of the $9.8 billion paid for leas-es in deep water - $2.6 billion stayed on the table.
Network graph displays Shell and Chevron (purple squares) results for the fall 2017 sale.