The identification of a bed's lithology is fundamental to all reservoir characterization because the physical and chemical properties of the rock that holds hydrocarbons and/or water affect the response of every tool used to measure formation properties. Understanding reservoir lithology is the foundation from which all other petrophysical calculations are made. To make accurate petrophysical calculations of porosity, water saturation (Sw), and permeability, the various lithologies of the reservoir interval must be identified and their implications understood. Lithology means "the composition or type of rock such as sandstone or limestone." Lithology focuses on grains, while rock type focuses on pores. The list of rock types contains more than 250 classifications. Another term used in the literature is the Greek equivalent "petrofacies."
In making the petrophysical calculations of lithology, net pay, porosity, water saturation, and permeability at the reservoir level, the development of a complete petrophysical database is the critical first step. This section describes the requirements for creating such a database before making any of these calculations. The topic is divided into four parts: inventory of existing petrophysical data; evaluation of the quality of existing data; conditioning the data for reservoir parameter calculations; and acquisition of additional petrophysical data, where needed. The overall goal of developing the petrophysical database is to use as much valid data as possible to develop the best standard from which to make the calculations of the petrophysical parameters. The second step in working with the petrophysical data is to evaluate the quality of each of these types of data. This step requires that the data inventory and database preparation steps are completed first so that this second step can occur as a systematic and complete process. The evaluation process is a "compare and contrast" exercise. The evaluation of log-data quality has many aspects. This should be noted in the petrophysical database. "Flags" of various types should be stored, for example, to denote intervals where the hole size exceeds some limit, or where there is cycle-skipping on the sonic logs. Logging tools sometimes become temporarily stuck as a log is being run. When the tool is stationary, each detector on it becomes stuck at a different depth, so the interval of "stuck" log will vary for each log curve. For example, the neutron log typically sticks over an interval approximately 10 ft above the stuck interval on a density log. It may be possible to "splice" in a replacement section of log from a repeated log section, or the invalid readings may simply be deleted. Second, each log is formally calibrated before the start of each logging run by various calibration standards. The logs are also checked again after the run. Calibration records may assist in determining the quality of the logs. Perhaps of equal importance are the written comments on the log heading made immediately after the job by the logging engineer. Third, systematic influences on the quality of log readings should be corrected. For example, if some of the wells are drilled with water-based mud (WBM), the effect of WBM-filtrate invasion on various resistivity logs can be quantified. This is done by computations made using the various resistivity logs in the same wellbore; however, where deep invasion of WBM filtrate occurs, offsetting wells drilled with oil-based mud (OBM) give a good comparison. The induction logs in OBM wells can provide accurate true reservoir resistivity values in thick hydrocarbon zones. See the chapter on resistivity and SP logging in this volume of the Handbook for more information on how invasion effects can be handled. Boreholes are not always right cylinders.
This article presents brief summaries of detailed petrophysical evaluations of several fields that have been described in the SPE and Soc. of Professional Well Log Analysts (SPWLA) technical literature. These case studies cover some of the complications that occur when making net-pay, porosity, and water saturation (Sw) calculations. Prudhoe Bay is the largest oil and gas field in North America with more than 20 billion bbl of original oil in place (OOIP) and an overlying 30 Tscf gas cap. In the course of this determination, an extensive field coring program was conducted, which resulted in more than 25 oil-based mud (OBM) cores being cut in all areas of the field and some conventional water-based mud (WBM) and bland-mud cores in other wells. The background geologic understanding of the major reservoir, the Ivishak or Sadlerochit, and various technical studies have been presented in a number of technical papers.
The primary physical mechanisms that occur as a result of gas injection are (1) partial or complete maintenance of reservoir pressure, (2) displacement of oil by gas both horizontally and vertically, (3) vaporization of the liquid hydrocarbon components from the oil column and possibly from the gas cap if retrograde condensation has occurred or if the original gas cap contains a relict oil saturation, and (4) swelling of the oil if the oil at original reservoir conditions was very undersaturated with gas. Gas injection is particularly effective in high-relief reservoirs where the process is called "gravity drainage" because the vertical/gravity aspects increase the efficiency of the process and enhance recovery of updip oil residing above the uppermost oil-zone perforations. The decision to apply immiscible gas injection is based on a combination of technical and economic factors. Deferral of gas sales is a significant economic deterrent for many potential gas injection projects if an outlet for immediate gas sales is available. Nevertheless, a variety of opportunities still exist. First are those reservoirs with characteristics and conditions particularly conducive to gas/oil gravity drainage and where attendant high oil recoveries are possible. Second are those reservoirs where decreased depletion time resulting from lower reservoir oil viscosity and gas saturation in the vicinity of producing wells is more attractive economically than alternative recovery methods that have higher ultimate recovery potential but at higher costs. And third are reservoirs where recovery considerations are augmented by gas storage considerations and hence gas sales may be delayed for several years. Nonhydrocarbon gases such as CO2 and nitrogen can and have been used. In general, calculation techniques developed for hydrocarbon-gas injection and displacement can be used for the design and application of nonhydrocarbon, immiscible gas projects. Valuing the use of such gases must include any additional costs related to these gases, such as corrosion control, separating the nonhydrocarbon components to meet gas marketing specifications, and using the produced gas as fuel in field operations. The conceptual aspects of the displacement of oil by gas in reservoir rocks are discussed in this section. There are three aspects to this displacement: gas and oil viscosities, gas/oil capillary pressure (Pc) and relative permeability (kr) data, and the compositional interaction, or component mass transfer, between the oil and gas phases.
The Prudhoe Bay field, located on the North Slope of Alaska, is the largest oil and gas field in North America. The main Permo-Triassic reservoir is a thick deltaic high-quality sandstone deposit about 500 ft thick with porosities of 15 to 30% BV and permeabilities ranging from 50 to 3,000 md. The field contains 20 109 bbl of oil overlain by a 35 Tcf gas cap. Under much of the oil column area, there is a 20- to 60-ft-thick tar mat located above the oil-water contact (OWC).
Techniques described in this page are classic methods for describing immiscible displacement assuming equilibrium between injected gas and displaced oil phases while accounting for differing physical characteristics of the fluids, the effects of reservoir heterogeneities, and injection/production well configurations. Included are modifications to typical displacement equations, evaluating sweep efficiency, and calculating performance. In simple calculations, the reservoir is treated in terms of average properties for volume of rock, and production performance is described on the basis of an average well. Black-oil-type reservoir simulation models use essentially these same techniques but, by means of 1D, 2D, or 3D cell arrays, account for areal and vertical variations in rock and fluid properties, well-to-well gravity effects, and individual well characteristics. More complex compositional models account for nonequilibrium conditions between injected and displaced fluids and can be used to describe individual well streams in terms of the compositions of the produced fluids.
This page discusses the primary manner in which the immiscible gas/oil displacement process has been used in qualitative terms. This is the use of gas injection high on structure to displace oil downdip toward the production wells that are completed low in the oil column. In many cases, an original gas cap was present, so the gas was injected into that gas cap interval (see Figure 1 for cross-sectional view of anticlinal reservoir with gas cap over oil column with dip angle α and thickness h). In this situation, the force of gravity is at work, trying to stabilize the downward gas/oil displacement process by keeping the gas on top of the oil and counteracting the unstable gas/oil viscous displacement process. If the oil production rate is kept below the critical rate, then the gas/oil contact (GOC) will move downward at a uniform rate.
This paper presents a case history of drilling automation system pilot deployment, inclusive of wired drill pipe on an Arctic drilling operation. This builds on the body of work that BP (the operator) previously presented in 2017 related to the deployment of an alternate drilling automation system. The focus will be on the challenges and lessons learned during this deployment over a series of development wells.
Two major aspects of technology were introduced during this pilot, the first being a drilling automation software platform that allowed secure access to the rig's drilling control system. This platform hosts applications that interpret the activity on the rig and issue control setpoints to drive the operation of the rig's top drive, mud pumps, auto driller, drawworks, and slips. The second component introduced was a wired drill string, which provides access to high speed delivery of downhole data from a series of distributed downhole sensors, providing an opportunity to improve both automated control and real-time interpretation of downhole phenomena.
The project team identified several key performance indicators both at the project level and for each well. The project level key performance indicators (KPIs) were designed to give the operator an understanding of the reliability and robustness of the hardware and software components of the automation system. The KPIs for the well were designed to assess the impact of the technology on drilling efficiency through aspects of invisible lost time reduction (connection and survey times). The well level KPIs also fed into the project KPIs by capturing uptime, reliability, and repeatability of the hardware and software components of the system.
The paper describes several specific examples of where the benefits of the technology were realized as related to the KPIs above and describes some of the technical challenges encountered and fixes employed during the pilot campaign.
The paper also gives an insight into some of the non-technical challenges related to deployment of this system, around human behavioral characteristics. It discusses how focused collaboration and communication from all the stakeholders was managed and directed towards a successful deployment.
The work delivered on this project incorporates several technological innovations that were deployed for the first time on an active drilling operation. Delivery of these were important milestones for both the operator and the automation technology provider as part of their collaboration to increase the capability and reliability of these systems. The operator believes that this effort is key to allowing its drilling operations to realize longer term and sustainable benefits from automation.
Most carbonate reservoirs have fractures which have a detrimental effect on sweep efficiency during oil recovery. The objective of this research is to block the big fractures with polymeric particles and divert the injection fluid into the matrix for better sweep efficiency during CO2 floods. Polymeric particles have been developed that swell as salinity is increased. These particles are termed SISPP or salinity induced swelling polymeric particles. SISPPs swell more in higher concentration brine contrary to common polymeric particle gels (PPGs) which shrink. Water flood and miscible floods are conducted in fractured cores with SISPP placed in the fractures. The SISPP placement increases oil recovery in fractured cores during high salinity water floods and miscible/CO2 floods. Furthermore, a model for particle swelling, and the concomitant change in permeability, as a function of brine salinity was implemented in UTCHEM, and single phase and oil recovery corefloods were modeled. UTCHEM simulations showed good agreement with the experimental results.
Li, Maowen (CNOOC) | Lei, Guowen (Baker Hughes, a GE Company) | Zhang, Mingjie (CNOOC) | Coskun, Sefer B. (Baker Hughes, a GE Company) | Sy, Resksmey C. (Baker Hughes, a GE Company) | Hardikar, Nikhil P. (Baker Hughes, a GE Company)
The WEIZHOU-XYZ project is a newly developed offshore oilfield located in South China Sea. A quick and accurate productivity estimate would add valuable information to the decision-making on further field development, which is divided into three phases as per priority. A luxury dataset comprising seismic, drilling, well logs, well testing and production was acquired from most wells drilled in Phase-1. The objective of this paper is to establish a fast productivity forecasting method that can be used for the newly drilled wells of Phase-2 and Phase-3 after acquiring logging-while-drilling (LWD) data only.
The well-productivity forecasting model was based on uncertainty analyses using the Monte-Carlo method. Starting with equations of the production rate and the productivity index, each parameter of the equations has been investigated based on LWD data, and with a reference to the Phase-1 wells. Two key reservoir data used for the forecasting model are LWD formation testing (formation pressure while drilling - FPWD) and LWD nuclear magnetic resonance (NMR). These two dataset are the main information collected while drilling along with LWD resistivity and gamma ray in Phase 2. The FPWD data provides mobility, thus indicating the ability of fluid flow through the permeable reservoirs. The LWD NMR data provide continuous porosity measurement. Additionally, the T2 relaxation sensitives to both the pore size distribution and fluid properties providing estimation of formation permeability and fluid viscosity. Differing from the conventional way of a constant value input to the production rate equation, the proposed method sets all productivity related parameters (permeability, thickness, formation volume factor, viscosity, drainage radius etc.) under an uncertainty distribution range. The productivity prediction model is processed and evaluated using Monte-Carlo simulations. A scenario of 10,000 runs were conducted to account for the possible production distribution. As a result, an expected value of production rate or productivity index is used for the delivery of the possible forecasting outcome.
In this study, a successful application of this forecasting model has been proven by a good match with actual results from a well test. The observed difference is less than 5% between the real production rate and the expected value from the forecasting model.
This paper shows that formation testing while drilling and NMR while drilling together provide valuable inputs for productivity forecasting. The integrated method would be very helpful and meaningful for the production evaluation and decision-making for the Phase-2 and Phase-3 development wells, which will have limited data acquisition. A Monte-Carlo simulation workflow has been proposed for