ABSTRACT

Testing of stacked-sand pay zones in the Gulf of Mexico (GoM) and other basins with similar depositional systems (e. g., offshore Nigeria, Bohai Bay) is quite costly when up to 11 sands need to be evaluated in order to determine testing and completion intervals. Other reservoir types such as fractured shale, carbonate, and fresh water reservoirs present challenging interpretation problems as well, especially in terms of assessing native hydrocarbon quality prior to testing and completion. With high daily rig rates and test costs, the ability to minimize idle time and testing expenses has direct economic impact on the cost of operating a well. In addition, identifying any potential bypassed pay zones provides additional economic benefit.

A variety of potential pay zone types is present in the GoM ranging from biogenic, thermogenic dry gas, wet gas, or condensate to normal, heavy, waxy, or biodegraded crude oils. Assessment of GoM sands is complicated by the fact that they are typically unconsolidated sediments and are often drilled with oil-based or synthetic muds, which make it difficult to evaluate the presence of reservoired hydrocarbons using conventional logging techniques.

Simple and inexpensive geochemical analyses provide information on reservoir hydrocarbons directly from prospective reservoir rock samples in about 15 minutes, thereby enhancing well site decision-making processes. Geochemical analyses of frozen cuttings or sidewall core (SWC) samples confirm the presence of native hydrocarbons and provide an assessment of hydrocarbon type (gas or oil) and quality (e. g., GOR, viscosity, API gravity).

The first goal of geochemical analyses for well site decision making is to identify or confirm prospective pay zones including any potential bypassed pay, in either water or oil-based mud systems. The second goal is to identify the likely type and quality of pay. Further, the comparison of condensates in rocks to produced fluids can be used to assess vertical fractionation of reservoirs, seal effectiveness, and for correlation or oil typing purposes. Finally, vertical connectivity of reservoirs, which may also play a role in completion decisions, can be assessed using these data.