RESERVOIR DEPLETION ASSESSMENT WHILE DRILLING

Abstract

Implementations described and claimed herein provide systems and methods for assessment of reservoir depletion based on mud gas geochemistry while a well is being drilled and to assist in optimizing development and production strategies with faster turnaround and lower cost. Contrary to drilling one or more test wells to determine the depletion of the reservoir, mud gas geochemistry data is readily available, real-time, and low cost. In some particular implementations, a total hydrocarbon gas intensity and/or detailed hydrocarbon gas compositions may be analyzed and function together as a reliable indicator of reservoir pressure and in-place petroleum fluids quality. The mud gas geochemistry data may be interpreted in the context of in-place petroleum fluids properties (e.g., maturity, bulk and isotope compositions, and PVT phase behaviors), with the aids of mud logging and production data from nearby wells if available, to evaluate reservoir depletion.

Description

FIELD

Aspects of the present disclosure relate generally to systems and methods for assessment of reservoir depletion by offset wells and, more particularly, to an assessment platform utilizing advanced mud gas geochemistry to assess reservoir depletion while a well is being drilled.

BACKGROUND

Modern reservoir management processes generally include setting strategy, development planning, implementing, monitoring, and evaluating results. Development planning in particular may include addressing subsurface and surface uncertainties, generating forecasts on production performance and economic values, and optimizing facilities design. There have been many established practices and tools for conventional field planning, including assessment, estimation, and/or prediction of reservoir depletion by one or more offset wells from a target well. Assessment of reservoir depletion by offset wells is critical for optimizing development and production strategies for unconventional plays, e.g., well spacing and stacking, well completion design, etc. Typically, assessment of reservoir depletion is accomplished only after new wells placed into a target reservoir have been completed and put on production, incurring both significant time and cost to determine the state of the reservoir.

It is with these observations in mind, among others, that various aspects of the present disclosure were conceived and developed.

SUMMARY

Implementations described and claimed herein address the foregoing problems by providing systems and methods for assessment of depletion of a reservoir. The method may include the operations of receiving, from a well being drilled, one or a multitude of mud samples from the well, determining, by a geofluid spectrometer (a system for geochemical characterizations of mud gas), at least one geochemical parameter of the one or many mud samples, wherein the at least one geochemical parameter comprises a total hydrocarbon gas intensity from the one or more mud samples, and assessing the depletion of a target reservoir of the well based at least on the at least one geochemical parameter of the one or more mud samples.

Other implementations are also described and recited herein. Further, while multiple implementations are disclosed, still other implementations of the presently disclosed technology will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative implementations of the presently disclosed technology. As will be realized, the presently disclosed technology is capable of modifications in various aspects, all without departing from the spirit and scope of the presently disclosed technology. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further advantages thereof, may best be understood by reference to the following description taken in conjunction with the accompanying figures by way of example and not by way of limitation, in which:

FIG. 1 shows an example network environment that may implement various systems and methods discussed herein.

FIG. 2 is a block diagram illustrating a system for assessing reservoir depletion while a well is being drilled using mud gas logging.

FIG. 3 shows an example block diagram of a reservoir depletion assessment platform.

FIG. 4 illustrates example operations for utilizing a reservoir depletion assessment platform to determine reservoir depletion using mud gas logging during drilling of a well.

FIG. 5 is an example mud gas logging of hydrocarbon gas and gas composition data from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 6 is an example mud gas logging of mud gas balance and wetness from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 7 is an example mud gas logging of nC5 concentration from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 8 is an example mud gas logging of nC7 concentration from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 9 is an example mud gas logging of nC7 concentration and Toluene content comparison from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 10 is an example mud gas logging of Toluene/nC7 and Benzene/nC6 ratios from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 11 is an example mud gas logging of CH/nC6 and Benzene/nC6 ratios from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 12 is an example mud gas logging of MCH/nC7 and Toluene/nC7 ratios from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 13 is an example mud gas logging of helium concentration from a plurality of wells utilized to assess reservoir depletion of an area.

FIG. 14 is a workflow for a PVT simulation that may be executed as a portion of the reservoir depletion assessment system.

FIG. 15 illustrates a plot of mean total hydrocarbon gas intensity versus the mean instantaneous shut-in pressures for several infill wells.

FIG. 16 shows an example computing system that may implement various systems and methods discussed herein.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments of the invention, one or more examples of which are illustrated in the accompanying drawings. Each example is provided by way of explanation of the invention, not as a limitation of the invention. It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For instance, features illustrated or described as part of one embodiment can be used on another embodiment to yield a still further embodiment. Thus, it is intended that the present invention cover such modifications and variations that come within the scope of the appended claims and their equivalents.

Aspects of the present disclosure involve systems and methods for assessment of reservoir depletion based on mud gas geochemistry while a well is being drilled and to assist in optimizing development and production strategies with faster turnaround and lower cost. Contrary to drill and test one or more wells to determine the depletion of the reservoir, mud gas geochemistry data is readily available, real-time, and low cost. In some particular implementations, a total hydrocarbon gas intensity and/or detailed hydrocarbon gas compositions may be analyzed and function together as a reliable indicator of reservoir pressure and in-place petroleum fluids quality, given the typical situation that: 1) petroleum charge in the target reservoir and reservoir properties are reasonably understood in general; and 2) engineering and production data from offset wells are available. The mud gas geochemistry data may be interpreted in the context of in-place petroleum fluids properties (e.g., maturity, bulk and isotope compositions, and pressure, volume, and temperature (PVT) phase behaviors), with the aids of mud logging and production data from nearby wells if available, to evaluate reservoir depletion. When a multitude of new wells placed into the same/nearby reservoir intervals are drilling, more reliable depletion assessment can be achieved based on the changes in mud gas geochemistry with respect to each well's relative position to the offset well and how long the offset well has been on production.

These and other advantages may become apparent from the discussion included herein.

To begin a detailed discussion of an example reservoir depletion assessment system, reference is made to FIG. 1. In particular, FIG. 1 illustrates an example network environment 100 for implementing the various systems and methods, as described herein. As depicted, a network 104 is used by one or more computing or data storage devices for implementing the systems and methods for a reservoir depletion assessment platform 102 that employs advanced mud gas logging (mud gas geochemistry) to assess reservoir depletion while a well is being drilled, based on many factors, such as total hydrocarbon gas intensity and mud gas compositions. In one implementation, various components of the reservoir depletion assessment platform 102, one or more user devices 106, one or more databases 110, and/or other network components or computing devices described herein are communicatively connected to the network 104. Examples of the user devices 106 include a terminal, personal computer, a smart-phone, a tablet, a mobile computer, a workstation, and/or the like.

A server 108 may, in some instances, host the system. In one implementation, the server 108 also hosts a website or an application that users may visit to access the network environment 100, including the reservoir depletion assessment platform 102. The server 108 may be one single server, a plurality of servers with each such server being a physical server or a virtual machine, or a collection of both physical servers and virtual machines. In another implementation, a cloud hosts one or more components of the system. The reservoir depletion assessment platform 102, the user devices 106, the server 108, and other resources connected to the network 104 may access one or more additional servers for access to one or more websites, applications, web services interfaces, etc. that are used for reservoir modeling.

FIG. 2 is a block diagram illustrating a system 200 for assessing reservoir depletion while a well is being drilled using mud gas logging. The system 200 of FIG. 2 may include more or fewer components than those illustrated. In the example shown, a suction probe 204 may be inserted in the drilling mud circulation system 202 while the well is being drilled. The suction probe 204 transfers the mud to a constant volume degasser (CVD) 206 for analysis. The mud received at the CVD 206 may be of a varying temperature such that the mud may be transferred through a mud temperature controller 208 to regulate the temperature of the siphoned mud for gas analysis. The temperature regulated mud may be returned to the CVD 206 for degassing. The released gasses may be transmitted through an insulated gas line 210 and through one or more filters 212 to a geofluid spectrometer 214, which may include but not limited to flame ionization detector (FID), thermal conductivity detector (TCD), and gas chromatography (GC) coupled with FID, TCD, laser spectrometer and/or mass spectrometer (MS), to determine the total hydrocarbon gas intensity and the mud gas composition, which includes but not limited to detailed composition of hydrocarbons from C1 up to C8 or higher, detailed composition of non-hydrocarbons like Helium, CO₂ and H₂S, and carbon isotope compositions of methane, ethane, propane, butane, pentane, etc., and CO₂. The results from the geofluid spectrometer 214 may then be transmitted to the reservoir depletion assessment platform 102 for analysis as described herein. It should be appreciated that other configurations of the components of the system 200 may also provide the mud gas logging data for the reservoir depletion assessment platform 102.

FIG. 3 shows an example block diagram of a reservoir depletion assessment system 300 for utilizing advanced mud gas logging data to assess reservoir depletion while a well is being drilled. In general, the system 300 may include a depletion assessment platform 306. In one implementation, the depletion assessment platform 306 may be a part of the reservoir depletion assessment platform 102 of FIG. 1. As shown in FIG. 3, the depletion assessment platform 306 may be in communication with a computing device 328 providing a user interface 330. As explained in more detail below, the depletion assessment platform 306 may be accessible to various users to assess reservoir depletion and other management processes of reservoirs. In some instances, access to the depletion assessment platform 306 may occur through the user interface 330 executed on the computing device 328.

The depletion assessment platform 306 may include a depletion assessment application 312 executed to perform one or more of the operations described herein. The depletion assessment application 312 may be stored in a computer readable media 310 (e.g., memory) and executed on a processing system 308 of the depletion assessment platform 306 or other type of computing system, such as that described below. For example, the depletion assessment application 312 may include instructions that may be executed in an operating system environment, such as a Microsoft Windows™ operating system, a Linux operating system, or a UNIX operating system environment. By way of example and not limitation, non-transitory computer readable medium 310 comprises computer storage media, such as non-transient storage memory, volatile media, nonvolatile media, removable media, and/or non-removable media implemented in a method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.

The depletion assessment application 312 may also utilize a data source 320 of the computer readable media 310 for storage of data and information associated with the depletion assessment platform 306. For example, the depletion assessment application 312 may store mud gas geochemical data from a well, offset well production and location data, and the like. As described in more detail below, such data may be stored and accessed via the user interface 330 for one or more users of the depletion assessment platform 306.

The depletion assessment application 312 may include several components to assess reservoir depletion during the drilling of a well. For example, the depletion assessment application 312 may include a spectrometer communicator 314 for communicating or otherwise receiving mud gas data and/or logging for use in assessing the depletion of a reservoir. In one implementation such as that illustrated in FIG. 2, the reservoir depletion assessment platform 102 may be in communication with the geofluid spectrometer 214 via the spectrometer communicator 314. As the geofluid spectrometer 214 determined the gasses within a mud sample taken from a well 202 during drilling, the results of the spectrometer may be transmitted to or otherwise provided to the reservoir depletion assessment platform 102. The depletion assessment application 312 may also communicate with other geofluid spectrometers or other devices configured to analyze mud gas compositions or store mud gas composition data, either from the well being drilled or other offset wells drilled earlier.

The depletion assessment application 312 may also include a total hydrocarbon gas analyzer 316 for analyzing hydrocarbon gas concentrations of the mud gas data received from the geofluid spectrometer. The hydrocarbon gas concentrations of the mud gas data of a well during drilling may be used to determine or estimate the depletion of a reservoir. Similarly, the depletion assessment application 312 may include a hydrocarbon gas compositions analyzer 318 for determining the compositions of the hydrocarbon gases of the mud sample as received from the geofluid spectrometer. Both the total hydrocarbon gas concentration and the hydrocarbon gas compositions may be analyzed to assess the depletion of a reservoir, as explained in more detail below.

In many instances, the depletion of a reservoir is due to previous operation of one or more offset wells from the currently drilled well. Thus, assessment of depletion for a reservoir may be enhanced or improved with information and/or data from the one or more offset wells. As such, the depletion assessment application 312 may include an offset wells data manager 320 configured to receive and store information of offset wells, such as past or current production data and/or location of the offset wells relative to the currently drilled well. Such data, as explained in more detail below, may be analyzed to support or add to the assessment of the depletion of a reservoir determined by the depletion assessment application 312. In particular, a depletion assessor 322 of the depletion assessment application 312 may receive data or other information from the total hydrocarbon gas analyzer 316, the hydrocarbon gas compositions analyzer 318, and/or the offset wells data manager 320 and determine or estimate a depletion of a reservoir based on the obtained data and other information.

It should be appreciated that the components described herein are provided only as examples, and that the depletion assessment application 312 may have different components, additional components, or fewer components than those described herein. For example, one or more components as described in FIG. 3 may be combined into a single component. As another example, certain components described herein may be encoded on, and executed on other computing systems. Further, more or fewer of the components discussed above with relation to the depletion assessment platform 306 may be included with the tool, including additional components or modules included to perform the operations discussed herein.

FIG. 4 illustrates example operations for utilizing a reservoir depletion assessment platform to determine reservoir depletion using mud gas logging while drilling of a well. The operations may be performed by a computing device configured to execute any algorithm, including equation-of-state (EOS) oriented modeling techniques. Such operations may be executed through control of one or more hardware components, one or more software programs like PVTSim, or a combination of both hardware and software components of the computing device. For example, the depletion assessor 322 or other component or combination of components of the depletion assessment platform 306 may perform or execute the operations described herein to determine or assess the depletion of a reservoir from mud gas analysis results of the well being drilled.

Beginning at operation 402, the computing device may obtain mud gas geochemistry data of a well while the well is being drilled. In one implementation, the system 200 of FIG. 2 may obtain mud gas logging data from a well as the well is being drilled, as described above. The geofluid spectrometer 214 of the system 200 may obtain various measurements of mud gasses from samples from the well 202, including total hydrocarbon gas intensity and compositions of hydrocarbon gasses, among other possible mud gas measurements. At operation 404, the computing device may analyze the total hydrocarbon gas intensity from the mud gas logging data received from a geofluid spectrometer. For example, FIG. 5 is an example mud gas logging of hydrocarbon gas and gas composition data from a plurality of wells utilized to assess reservoir depletion of an area. Graph 502 shows a typical interpretative plot of mud gas logging result from the first well (“Well A”). Total hydrocarbon gas intensity, when interpreted in the context of rate of penetration (ROP) of drilling, which is also plotted (black curve) together with total hydrocarbon gas intensity (red curve) in the left track of Graph 502, is a strong indicator of hydrocarbon charges in petroleum reservoir. When everything else is the same, drilling into a fully charged reservoir has higher total hydrocarbon gas intensity, whereas drilling into depleted reservoir has lower total hydrocarbon gas intensity. The right track of Graph 502 shows the petroleum fluid quality indicator,calculated as the ratio of C1/C3 of the hydrocarbon gas in the mud gas. Mud gas composition is a strong indicator of reservoir fluid quality, and when interpreted in the context of reservoir pressure and temperature conditions and fluid's phase behaviors, is a reliable indicator of reservoir depletion, which will be discussed herein. Graph 602 shows another interpretative plot for the composition of hydrocarbons in mud gas from Well A, in which hydrocarbon gas wetness and balance are plotted against each other. Wetness is calculated as: (C2+C3+iC4+nC4+iC5+nC5)/(C1+C2+C3+iC4+nC4+iC5+nC5)*100. Balance is calculated as: (C1+C2)/(C3+iC4+nC4+iC5+nC5). This plot is highly indicative of reservoir fluid quality (e.g, oil in place or gas in place), and when interpreted in local geological settings, reservoir pressure and temperature conditions, and fluid's phase behavior, can yield highly valuable information regarding reservoir depletion status, which will be discussed herein.

At operation 406, the computing device may analyze the hydrocarbon gas compositions from the mud gas logging data received from a geofluid spectrometer. For example, FIG. 7 is an example mud gas logging of C1 and nC5 concentrations plotted together for a plurality of wells utilized to assess reservoir depletion of an area. Graph 702 illustrates the co-variation of C1 and nC5 concentrations in the mud gas for well A at varying depths as the well is drilled. As discussed in more detail below, such mud gas information may be used to assess or estimate depletion of a reservoir. At operation 408, the analysis of the drilled well may be compared to similar analysis to one or more offset wells associated with the well being drilled. For example, the offset wells data manager 320 may store or obtain production data of the one or more offset wells to aid in determining the depletion of the reservoir. In some implementations, such offset well data may be obtained for previous operation of the offset wells. In another implementation, the offset well data may include mud gas logging data as the offset wells were drilled. In still other implementations, the offset wells data may not be obtained or analyzed by the depletion assessment platform 306 such that the reservoir depletion may be assessed based on the mud gas logging data from the target well alone. Offset wells data may also include relative location of the offset wells from the target well and how long the offset wells have been on production.

At operation 410, an assessment of the depletion of a reservoir during drilling of a well may be determined from the above analysis. In particular, the mud gas logging data obtained or determined above, such as total hydrocarbon gas intensity and/or mud gas compositions, may be used to estimate or assess a depletion of the reservoir. In some instances, the mud gas geochemistry data may be interpreted in the context of in-place petroleum fluids properties (e.g., maturity, bulk and isotope compositions, and PVT phase behaviors), with the aids of mud logging and production data from nearby offset wells if available, to further refine the evaluation of the reservoir depletion. In general, the total hydrocarbon gas intensity and detailed hydrocarbon gas compositions obtained during drilling of the well may function together as a reliable indicator of reservoir pressure and in-place petroleum fluids quality, given the typical situation that: 1) petroleum charge in the target reservoir and reservoir properties are reasonably understood in general; and 2) engineering and production data from offset wells are available. When a multitude of new wells placed into the same/nearby reservoir intervals are drilling, more reliable depletion assessment can be achieved based on the changes in mud gas geochemistry with respect to each well's relative position to the offset well and how long the offset well has been in production. In this manner, the assessment of the depletion of the reservoir may be reinforced or further refined based on the offset well data, including the relative location of the offset wells to the well being drilled. As such, through the method 400 of FIG. 4, a depletion of a petroleum reservoir by production wells nearby may be assessed based on mud gas geochemistry during drilling to assist in optimizing development and production strategies with faster turnaround and lower cost. The method is significantly more cost effective and faster than conventional approaches based on production and engineering data, e.g., bottom hole pressure, production rate, and gas oil ratio (GOR).

Several aspects of mud gas geochemistry, such as total hydrocarbon gas intensity and detailed hydrocarbon gas compositions, may be analyzed to assess reservoir depletion. For example, FIGS. 5-14 illustrate various mud gas readings or interpretations that relate to a reservoir depletion and may be obtained while drilling a well. In particular, FIG. 5 illustrates total hydrocarbon gas intensity and gas richness for four wells, namely well A (502), well B (504), well C (506), and well D (508). As shown, the total hydrocarbon gas intensity is lowest for well D and highest for well B. Similarly, well D displays the leanest richness and well B is the richest. In general, the total hydrocarbon gas intensity responds fastest to reservoir depletion while gas richness changes slower. Thus, through this analysis, well B is assessed to be the least depleted reservoir and verification of the reservoirs of the wells bear out this conclusion.

Additional data may also be analyzed or considered by the reservoir depletion assessment platform 102. For example, FIG. 6 illustrates plots of mud gas balance and wetness ratios from the plurality of wells A-D (Graphs 602-608) utilized to assess reservoir depletion of an area. In general, upon passing through constant volume degasser 206, mud gas is significantly wetter than mud gas sampled with a conventional mud gas buster. The mud gas balance and wetness may be analyzed to provide information on the depletion of a reservoir. For example, the plots illustrate well B (Graph 604) with the lowest mud gas balance (light-to-heavy ratio) and the highest wetness, indicating well B as the least depleted reservoir.

FIG. 7 illustrates several plots 702-708 of nC5 concentration vs. C1 concentration from a plurality of wells (wells A-D) that may be utilized to assess reservoir depletion of an area. As illustrated, the nC5 concentration is highest for the plot of well B (704) and lowest for the plot of well D (708). The high concentration of nC5 for well B indicates that the reservoir of well B is least depleted while the reservoir for well D is the most depleted. Similarly, FIG. 8 illustrates several plots 802-808 of nC7 concentration vs. C1 concentration from a plurality of wells (wells A-D) that may be utilized to assess reservoir depletion of an area. As illustrated, the nC7 concentration is highest for the plot of well B (804) and lowest for the plot of well D (808). The high concentration of nC7 for well B indicates that the reservoir of well B is least depleted while the reservoir for well D is the most depleted. The same result will be derived by interpreting the data in terms of nC5/C1 and nC7/C1 ratios for FIGS. 7 and 8, respectively. Further, it is noted that nC7 has larger concentration gradient across the reservoir than nC5 (bigger difference between well D and well C).

FIG. 9 illustrates several plots 902-908 of nC7 and Tolucene content comparison from a plurality of wells (wells A-D) that may be utilized to assess reservoir depletion of an area. As illustrated, the nC7 concentration and Tolucene concentration is highest for the plot of well B and lowest for the plot of well D, indicating different degrees of reservoir depletion.

Advanced mud gas geochemistry can be utilized to assess reservoir water saturation differences, in response to either original petroleum charge or reservoir depletion upon production by offset wells. FIG. 10 illustrates several plots 1002-1008 of a water saturation indicators Tolucene/nC7 ratio and Benzene/nC6 ratio from a plurality of wells (wells A-D) in an area. In general, hydrocarbon compounds with smaller number of carbons have higher water solubility than those with larger number of carbons, e.g., nC6 is more water soluble than nC7, and benzene is more water soluble than toluene. For hydrocarbons with the same number of carbons, aromatic compounds are more water soluble than the aliphatic compounds, e.g., toluene is more water soluble than nC7. Thus, interactions of petroleum fluids with water will leave geochemical imprints in petroleum fluids, which can be picked up in mud gas and leveraged to diagnose reservoir conditions. As illustrated, the Tolucene/nC7 ratio and Benzene/nC6 ratio are highest for more depleted reservoirs, such as well C and well D. Noteworthy, the spread between these two ratios is also very informative for reservoir depletion assessment, as different compounds react to water saturation change differently. With advanced mud gas logging data, additional compounds are available for similar water saturation and related reservoir depletion evaluation, thus enabling more robust assessment. FIG. 11 (1102-1108) shows a comparison of cyclohexane (CH) to nC6 and benzene to nC6 ratios. FIG. 12 (1202-1208) shows a comparison of methylcyclohexane (MCH) to nC7 and toluene to nC7 ratios. All corroborate the same conclusion or assessment as above.

Using advanced mud gas logging, the helium content in the mud gas can be quantified and used as a reservoir porosity indicator, especially for porosity associated with fractures. This porosity indicator can further enhance the reservoir depletion assessment based on total hydrocarbon gas intensity. Typically, when everything else is identical, mud gas from reservoir with higher porosity will have stronger total hydrocarbon gas intensity. FIG. 13 illustrates several plots 1302-1308 of concentrations of helium from a plurality of wells utilized to assess reservoir depletion of an area s obtained from a plurality of wells (wells A-D) that may be utilized to assess reservoir depletion of an area. As illustrated, the helium concentration is lowest for the plot of well B (1304) than the other wells, indicating that the high total hydrocarbon from reservoir at well B is not due to higher porosity, but least reservoir depletion. These and other mud gas geochemistry measurements may be used to assess, estimate, or determine depletion of a reservoir as the well is being drilled, with reduced cost and faster turn-around time than other methods typically used to estimate reservoir depletion.

As noted above, the reservoir depletion assessment system may include pressure, volume, and temperature (PVT) phase behavior simulations to interpret the mud gas data and streamline the workflow for the reservoir depletion analysis. For example, FIG. 14 is a workflow 1400 for a PVT simulation that may be executed as a portion of the reservoir depletion assessment system. The workflow 1400 may include establishing reservoir fluids composition at operation 1402 and drilling mud compositions at operation 1404. The reservoir fluids and drilling mud with established compositions may be mixed at certain mixing ratio under downhole PT condition at operation 1406. At operation 1408, the workflow may flash the mixture from operation 1406 to the mud pit PT condition and, at operation 1410, the workflow may flash the liquid phase from operation 1408 to the degasser PT condition. Following the flash to the degasser PT condition, the workflow at operation 1410 may output the simulated mud gas compositions and the estimated amount of original reservoir fluids liberated as hydrocarbon gas. The workflow 1400 thus attempts to match vapor phase composition with mud gas composition. In general, the vapor phase as a weighted percentage of mixed reservoir fluid correlates to the total hydrocarbon gas intensity. Thus, taking the full mud circulation cycle and drilling and mud logging settings into account, this workflow 1400 predicts that total hydrocarbon intensity and hydrocarbon gas richness will co-vary in a self-consistent fashion, as observed among real-world well locations due to different levels of reservoir depletion.

The depletion assessment described above may be cross-checked with instantaneous shut-in pressure (ISIP) upon completion of the infill wells. Assuming all other conditions are equal, higher ISIP suggests higher reservoir pressure, thus less depletion. FIG. 15 illustrates a plot 1500 of the mean total hydrocarbon gas intensity along the lateral against the mean ISIPs of all completion stages for several infill wells, labeled 701H, 702H, 703H and 704H in the plot. As illustrated, infill well 703H has the highest mean ISIP of 5840 psi whilst infill well 701H has the lowest mean ISIP of 5073 psi. The mean ISIP for infill wells 702H and 704H is 5618 psi and 5483 psi, respectively. A strong positive correlation between ISIP and total hydrocarbon gas intensity is evident. Dashed line 1502 illustrates an output of the model described above to model the positive correlation between ISIP and total hydrocarbon gas intensity. This comparison to the real-world infill wells demonstrates that assessment with quantitative mud gas data is consistent with pressure data-based assessment of reservoir depletion.

Referring to FIG. 16, a detailed description of an example computing system 1600 having one or more computing units that may implement various systems and methods discussed herein is provided. The computing system 1600 may be applicable to the reservoir depletion assessment platform 102 of FIG. 1, the system 100, and other computing or network devices. It will be appreciated that specific implementations of these devices may be of differing possible specific computing architectures not all of which are specifically discussed herein but will be understood by those of ordinary skill in the art.

The computer system 1600 may be a computing system capable of executing a computer program product to execute a computer process. Data and program files may be input to the computer system 1600, which reads the files and executes the programs therein. Some of the elements of the computer system 1600 are shown in FIG. 16, including one or more hardware processors 1602, one or more data storage devices 1604, one or more memory devices 1608, and/or one or more ports 1608-1610. Additionally, other elements that will be recognized by those skilled in the art may be included in the computing system 1600 but are not explicitly depicted in FIG. 16 or discussed further herein. Various elements of the computer system 1600 may communicate with one another by way of one or more communication buses, point-to-point communication paths, or other communication means not explicitly depicted in FIG. 16.

The processor 1602 may include, for example, a central processing unit (CPU), a microprocessor, a microcontroller, a digital signal processor (DSP), and/or one or more internal levels of cache. There may be one or more processors 1602, such that the processor 1602 comprises a single central-processing unit, or a plurality of processing units capable of executing instructions and performing operations in parallel with each other, commonly referred to as a parallel processing environment.

The computer system 1600 may be a conventional computer, a distributed computer, or any other type of computer, such as one or more external computers made available via a cloud computing architecture. The presently described technology is optionally implemented in software stored on the data stored device(s) 1604, stored on the memory device(s) 1606, and/or communicated via one or more of the ports 1608-1610, thereby transforming the computer system 1600 in FIG. 16 to a special purpose machine for implementing the operations described herein. Examples of the computer system 1600 include personal computers, terminals, workstations, mobile phones, tablets, laptops, personal computers, multimedia consoles, gaming consoles, set top boxes, and the like.

The one or more data storage devices 1604 may include any non-volatile data storage device capable of storing data generated or employed within the computing system 1600, such as computer executable instructions for performing a computer process, which may include instructions of both application programs and an operating system (OS) that manages the various components of the computing system 1600. The data storage devices 1604 may include, without limitation, magnetic disk drives, optical disk drives, solid state drives (SSDs), flash drives, and the like. The data storage devices 1604 may include removable data storage media, non-removable data storage media, and/or external storage devices made available via a wired or wireless network architecture with such computer program products, including one or more database management products, web server products, application server products, and/or other additional software components. Examples of removable data storage media include Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc Read-Only Memory (DVD-ROM), magneto-optical disks, flash drives, and the like. Examples of non-removable data storage media include internal magnetic hard disks, SSDs, and the like. The one or more memory devices 1606 may include volatile memory (e.g., dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and/or non-volatile memory (e.g., read-only memory (ROM), flash memory, etc.).

Computer program products containing mechanisms to effectuate the systems and methods in accordance with the presently described technology may reside in the data storage devices 1604 and/or the memory devices 1606, which may be referred to as machine-readable media. It will be appreciated that machine-readable media may include any tangible non-transitory medium that is capable of storing or encoding instructions to perform any one or more of the operations of the present disclosure for execution by a machine or that is capable of storing or encoding data structures and/or modules utilized by or associated with such instructions. Machine-readable media may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more executable instructions or data structures.

In some implementations, the computer system 1600 includes one or more ports, such as an input/output (I/O) port 1608 and a communication port 1610, for communicating with other computing, network, or reservoir development devices. It will be appreciated that the ports 1608-1610 may be combined or separate and that more or fewer ports may be included in the computer system 1600.

The I/O port 1608 may be connected to an I/O device, or other device, by which information is input to or output from the computing system 1600. Such I/O devices may include, without limitation, one or more input devices, output devices, and/or environment transducer devices.

In one implementation, the input devices convert a human-generated signal, such as, human voice, physical movement, physical touch or pressure, and/or the like, into electrical signals as input data into the computing system 1600 via the I/O port 1608. Similarly, the output devices may convert electrical signals received from computing system 1600 via the I/O port 1608 into signals that may be sensed as output by a human, such as sound, light, and/or touch. The input device may be an alphanumeric input device, including alphanumeric and other keys for communicating information and/or command selections to the processor 1602 via the I/O port 1608. The input device may be another type of user input device including, but not limited to: direction and selection control devices, such as a mouse, a trackball, cursor direction keys, a joystick, and/or a wheel; one or more sensors, such as a camera, a microphone, a positional sensor, an orientation sensor, a gravitational sensor, an inertial sensor, and/or an accelerometer; and/or a touch-sensitive display screen (“touchscreen”). The output devices may include, without limitation, a display, a touchscreen, a speaker, a tactile and/or haptic output device, and/or the like. In some implementations, the input device and the output device may be the same device, for example, in the case of a touchscreen.

The environment transducer devices convert one form of energy or signal into another for input into or output from the computing system 1600 via the I/O port 1608. For example, an electrical signal generated within the computing system 1600 may be converted to another type of signal, and/or vice-versa. In one implementation, the environment transducer devices sense characteristics or aspects of an environment local to or remote from the computing device 1600, such as, light, sound, temperature, pressure, magnetic field, electric field, chemical properties, physical movement, orientation, acceleration, gravity, and/or the like. Further, the environment transducer devices may generate signals to impose some effect on the environment either local to or remote from the example computing device 1600, such as, physical movement of some object (e.g., a mechanical actuator), heating or cooling of a substance, adding a chemical substance, and/or the like.

In one implementation, a communication port 1610 is connected to a network by way of which the computer system 1600 may receive network data useful in executing the methods and systems set out herein as well as transmitting information and network configuration changes determined thereby. Stated differently, the communication port 1610 connects the computer system 1600 to one or more communication interface devices configured to transmit and/or receive information between the computing system 1600 and other devices by way of one or more wired or wireless communication networks or connections. Examples of such networks or connections include, without limitation, Universal Serial Bus (USB), Ethernet, Wi-Fi, Bluetooth®, Near Field Communication (NFC), Long-Term Evolution (LTE), and so on. One or more such communication interface devices may be utilized via the communication port 1610 to communicate one or more other machines, either directly over a point-to-point communication path, over a wide area network (WAN) (e.g., the Internet), over a local area network (LAN), over a cellular (e.g., third generation (3G) or fourth generation (4G) or fifth generation (5G) network), or over another communication means. Further, the communication port 1610 may communicate with an antenna or other link for electromagnetic signal transmission and/or reception.

In an example implementation, reservoir depletion assessment platform, software, and other modules and services may be embodied by instructions stored on the data storage devices 1604 and/or the memory devices 1606 and executed by the processor 1602. The computer system 1600 may be integrated with or otherwise form part of the reservoir depletion assessment platform 102.

The system set forth in FIG. 16 is but one possible example of a computer system that may employ or be configured in accordance with aspects of the present disclosure. It will be appreciated that other non-transitory tangible computer-readable storage media storing computer-executable instructions for implementing the presently disclosed technology on a computing system may be utilized.

In the present disclosure, the methods disclosed may be implemented as sets of instructions or software readable by a device. Further, it is understood that the specific order or hierarchy of steps in the methods disclosed are instances of example approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the method can be rearranged while remaining within the disclosed subject matter. The accompanying method claims present elements of the various steps in a sample order, and are not necessarily meant to be limited to the specific order or hierarchy presented.

The described disclosure may be provided as a computer program product, or software, that may include a non-transitory machine-readable medium having stored thereon instructions, which may be used to program a computer system (or other electronic devices) to perform a process according to the present disclosure. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium, optical storage medium; magneto-optical storage medium, read only memory (ROM); random access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or other types of medium suitable for storing electronic instructions.

While the present disclosure has been described with reference to various implementations, it will be understood that these implementations are illustrative and that the scope of the present disclosure is not limited to them. Many variations, modifications, additions, and improvements are possible. More generally, embodiments in accordance with the present disclosure have been described in the context of particular implementations. Functionality may be separated or combined in blocks differently in various embodiments of the disclosure or described with different terminology. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure as defined in the claims that follow.

Claims

1. A method for assessment of depletion of a reservoir, the method comprising: receiving, from a well being drilled, one or more mud gas samples from the well;determining, by a geofluid spectrometer, at least one geochemical parameter of the one or more mud gas samples, wherein the at least one geochemical parameter comprises a total hydrocarbon gas intensity of the one or more mud gas samples; andassessing a depletion of a target reservoir of the well based at least on the at least one geochemical parameter of the one or more mud gas samples.

2. The method of claim 1 further comprising: obtaining, using a processing device, offset well production data, wherein the assessing the depletion of the target reservoir of the well is further based on the offset well production data.

3. The method of claim 1 further comprising: obtaining, using a processing device, a location of an offset well relative to a location of the well being drilled, wherein the assessing the depletion of the target reservoir of the well is further based on the location of the offset well and a duration the offset well has been producing.

4. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a concentration of C1 of the one or more mud gas samples.

5. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a concentration of C2 of the one or more mud gas samples.

6. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a concentration of C3 of the one or more mud gas samples.

7. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises concentrations of iC4 and nC4 of the one or more mud gas samples.

8. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises concentrations of iC5 and nC5 of the one or more mud gas samples.

9. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises concentrations of nC6, cyclo-hexane (CH) and benzene of the one or more mud gas samples.

10. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises concentrations of nC7, methyl cyclo-hexane (MCH) and toluene of the one or more mud gas samples.

11. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises concentrations of non-hydrocarbon gases, CO2, or H2S, of the one or more mud gas samples.

12. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a mud gas balance ratio and a wetness ratio of the one or more mud gas samples.

13. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a C1 to C3 ratio of the one or more mud gas samples.

14. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a ratio of benzene to nC6 of the one or more mud gas samples.

15. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a ratio of toulene to nC7 of the one or more mud gas samples.

16. The method of claim 1, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a water saturation parameter determined from a ratio of MCH to nC7 and a ratio of toluene to nC7 of the one or more mud gas samples.

17. The method of claim 1 further comprising: displaying, in a user interface, the assessment of the depletion of the target reservoir.

18. A system for assessment of depletion of a reservoir, the system comprising: a mud sample extraction device; anda reservoir depletion assessment platform including an application to: receive, from mud sample extraction device, one or more mud gas samples from the well;generate, based on at least one geochemical parameter of the one or more mud gas samples, an assessment of a depletion of a target reservoir of the well, wherein the at least one geochemical parameter comprises a total hydrocarbon gas intensity of the one or more mud gas sample; andassess a depletion of a target reservoir of the well based at least on the at least one geochemical parameter of the one or more mud gas samples.

19. The system of claim 18, wherein the application is further to: obtain offset well production data, wherein the assessing the depletion of the target reservoir of the well is further based on the offset well production data.

20. The system of claim 18, wherein the application is further to: obtain a location of an offset well relative to a location of the well being drilled, wherein the assessing the depletion of the target reservoir of the well is further based on the location of the offset well and a duration the offset well has been producing.

21. The system of claim 18, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a mud gas balance and a wetness variable of the one or more mud gas samples.

22. The system of claim 18, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a concentration of C1 of the one or more mud gas samples.

23. The system of claim 18, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a concentration of C2 of the one or more mud gas samples.

24. The system of claim 18, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a concentration of C3 of the one or more mud gas samples.

25. The system of claim 18, wherein the at least one geochemical parameter of the one or more mud gas samples further comprises a water saturation parameter determined from a ratio of MCH to nC7 and a ratio of toluene to nC7 of the one or more mud gas samples.

26. The system of claim 18, wherein the application is further to: display, in a user interface, the assessment of the depletion of the target reservoir.